Sub-picojoule-per-bit volitional neuromorphic devices for precise targeting and tracking

Domain: physics
Published: 2026-01-09
Editorial outcome: major_revision

Abstract
The technological revolution driven by artificial intelligence has significantly improved the hardware performance, but energy consumption remains a critical bottleneck. The state-of-the-art retinomorphic devices, as core components of artificial intelligence hardware, excel in feature extraction but are constrained by passive attention mechanisms that lack flexibility of actively extracting additional features. Inspired by the human visual system, this work introduces a volitional neuromorphic device with active volitional attention regulation. By leveraging gate-voltage-tunable photoconductance to generate adjustable differential spectral response and employing neural networks to evaluate spectral reconstruction accuracy, the device achieves selective task perception. Experimental results demonstrate a data compression ratio of 1.17% and an extreme information energy efficiency of 0.625 pJ/bit. This advancement not only advances retinomorphic hardware design but also provides a sustainable pathway for energy-efficient hyperspectral imaging and next-generation neuromorphic computing systems.

Introduction
The current landscape of technological revolution, featured by the astonishing achievements of artificial intelligence (AI) technologies, is ushering in a groundbreaking epoch1–3. State-of-the-art AI hardware, rooted in the classic Von Neumann architecture, showcases unparalleled performance but at a considerable energy cost4–6. For instance, cutting-edge H100 GPUs are projected to consume over 13 terawatt-hours of energy annually7,8, underscoring a critical concern for energy efficiency, particularly in data-driven era.

Human visual system, renowned for its remarkable efficiency in perceiving, transducing, and interpreting information at approximately 1.0 pJ/bit9–12, provides a promising avenue to tackle the aforementioned concern. This efficiency stems from the intricate mechanisms of visual attention, encompassing both passive autonomic attention (PAA) and active volitional attention (AVA)13,14. As illustrated in Fig. 1a, PAA, naturally attracted by external stimuli, preferentially dominates the process of forming ideology directly from significant signal attention. It involves the fundamental light signal receiving, converting and pre-processing, realizing the primary motion detection. The AVA is further intervened as the subjective selecting command is sent to the brain’s central thinking region guided by prior knowledge and objectives. Accordingly, visual attention is directed through priority maps to efficiently focus on key information while ignoring irrelevant stimuli, which facilitates selective feature extraction and thus an extreme energy efficiency. Leveraging insights from the human visual system hold immense significance in the development of intelligent devices15–18.

Encouragingly, the integration of visual attention into two-dimensional materials-based retinomorphic vision devices has demonstrated milestone breakthroughs, navigating versatile and complicated scenarios, such as intelligent imaging, in-sensor computing, all-in-one hardware and etc19–26. Nevertheless, they predominantly rely on PAA, constraining their capacity for efficient feature extraction. This results in redundant sensory data and heightened power consumption, especially in multi-sport scenarios such as road tracking, biology follows, adaptive cruise control, etc27. which highlights the critical necessity of developing volitional neuromorphic devices with exceptional energy efficiency28.

Here, we demonstrate volitional neuromorphic devices with extreme energy efficiency of sub-picojoules per bit by emulating the hierarchical functions of the human visual system. In addition to PAA for dynamic feature extraction, AVA empowers our devices to precisely target specific objects and track their trajectories based on spectral features with an average accuracy over 93%. Such high precision originates from the active feedback and correction attained by optimizing gate-tunable differential spectral photo-response, inspired by biological transsaccadic memory. As a result, the volitional neuromorphic devices exhibit a data compression ratio of 1.17%, minimizing redundant data while approaching the IEE of the human visual system at 0.625 pJ/bit. This advancement is poised to redefine the landscape of AI hardware development, emphasizing brain-like energy efficiency in non-Von Neumann architecture-based systems.

Results
Design of volitional neuromorphic devices
Given that the participation of AVA enables a high operation efficiency and low power consumption, we proposed volitional neuromorphic devices with extreme energy efficiency by simulating the fovea response to regions of interest, configured with a van der Waals heterostructure of MoSe2/h-BN/MoS2 (Fig. S1). The structural, interfacial, morphological and elementary characteristics are evaluated by conducting transmission electron microscope (TEM), Raman, X-ray photoelectronspectroscopy (XPS), atomic force microscope (AFM) and energy dispersive X-rayspectroscopy (EDS), respectively. As shown in Fig. 1c, clear interfaces and hierarchical element distribution are observed in the vdW heterostructure. The corresponding characteristic Eg (383 cm−1, 233 cm–1 and 1350 cm–1) and A1g (408 cm−1) peaks are identified for few-layer MoS2 flakes (as floating gate), MoSe2 flakes (as conduction channel) and h-BN flakes (as potential barrier), respectively (Fig. 1d)29–31. To further evaluate the interfacial characteristics, the resolved XPS spectra of Mo 3d, Se 3d, S 2p, B 1s and N 1s core levels are analyzed in Fig. 1e29–31, and the symmetric peak profiles of h-BN suggest that vdW interactions predominantly govern the interfacial characteristics32. In addition, the morphology profile and element distribution are recorded in Fig. S2, S3, further confirming the successful fabrication of proposed heterostructure with floating gate. As the aforementioned AVA in human visual system (Fig. 1a), the fovea reflects the polarity peak change of the cone photoreceptor, ensuring the fundamental information pre-processing. When an attention is focused on a region of interest, the corresponding neuronal potential activity is immediately enhanced and feeds back to the cone photoreceptors, promoting selective task perception and cognition. Similarly, as shown in Fig. 1b, the device-level AVA is proposed by introducing an active spectral feedback circulation, which progressively manipulates voltage feedback until outputs optimal spectral reconstruction accuracy. Specifically, MoSe2 photoactive channel receives optical signals and simulates the weight of synapse, and MoS2 conduction channel maintains the persistent current to realize the memory function. Subsequently, programmable gate voltage (Vg) pulses, serving as electrical stimulus, are applied to modulate the photoconductance, which generate both non-volatile positively and negatively photoconductive photocurrent (PPC and NPC) over visible spectrum to achieve polarity regulation (similar to cone cells). The differential operation between NPC and PPC photoconductive currents enables polarity-regulated spectral reconstruction by synergistically suppressing interference and amplifying target signals. For the same reason, to observe specific objects within all spectral information, the input voltage is carefully adjusted in the manner of designated optimal Vg for each wavelength, referring to the specific wavelength with the maximum reconstructed accuracy. Accordingly, only the spectral signal of specific interest can be emphasized instead of another irrelevant spectrum. This aligns with the natural way human processes and prioritizes visual information, ultimately enabling the targeting and localization of objects (e.g., a runner wearing a blue shirt). This can maintain 93% spectral recognition accuracy even under 1.17% data compression by dynamically balancing noise suppression and feature retention. Such operation guarantees an intelligent detection with subjective feature selections, which significantly reduces irrelevant redundant sensory data while effectively allocates processing resources to reduce energy consumption. As a result, AVA-involved volitional neuromorphic device adheres to the intelligent philosophy of the human brain that prioritizes appointed spectral sensory inputs over others, leading to an extreme energy efficiency, which provides an innovative way to establish autonomous selection of targeting and tracking.

Versatile characteristics of volitional neuromorphic devices
The AVA-involved sensing, memory, and computing capabilities are essential prerequisites in volitional neuromorphic devices, which ensure accurate targeting and tracking with extreme energy efficiency33,34. The intrinsic responsive characteristics are priorly examined under illumination with primary colors, as shown in Fig. 2a. The as-fabricated device demonstrates an obvious photoresponse that over two orders of magnitude larger than dark current, accompanied by exceptional responsivity, detectivity and noise-equivalent-power with a low incident light power of 0.1 mW (Figs. S4–S6). The photoconductive behaviors are subsequently evaluated by applying programmable Vg pulses (Fig. 2b, c). A single Vg pulse is initially applied for 1 s, and the light is simultaneously switched on once disconnecting the gate voltage. It is clear that both PPC and NPC are of progressive output, good uniformity and reproducibility. Notably, their response are much more prompt (rise time < 160 μs, Fig. S7) than HVS (~50 ms), which effectively protects from capturing ghost images, especially in the scenario of high-speed motion recognition35,36. Good photoresponsive characteristics and floating gate effect are also validated in the manner of pulse number and bias voltage (Figs. S8–S10). Meanwhile, this device possesses a linear memory window of ~80 V with a large stored charge density of 5.65 × 1012 cm–2, and maintains a good non-volatile stability (Fig. S11), which is beneficial for successive differential operation. These attractive data-perceiving and storing capabilities are originated from the appropriate arrangement of vdW heterojunction that allows an efficient Fowler-Nordheim tunneling. The corresponding band alignment and carrier dynamics are elucidated in Fig. S12. Briefly, electrons and holes initially undergo accumulating (MoSe2 conduction channel) and trapping (MoS2 modulation channel) processes as applying negative Vg. The electrons subsequently tunnel to MoS2 followed by the recombination with the trapped holes upon light illumination. Accordingly, the decreased number of electrons weakens the photoconductivity, resulting in a reduced photocurrent (i.e., NPC) and vice versa for PPC37.

The corresponding spectral response database is established via adequately mapping the photoresponse as functions of spectrum (450–800 nm) and gate voltage (−40 to 40 V), laying a foundation for the proposed device-level AVA feedback. As shown in Fig. 2d, PPC clearly transforms to NPC as tuning the direction of in-vertical electric field by switching Vg from positive to negative, which is consistent with a aforementioned device physics. Notably, both PPC and NPC possess a gradual response to both distinctive wavelength and gate voltage, indicating effective photoconductive modulation that enables the subsequent differential operation and thus spectral reconstruction. As mentioned earlier, differential current (Idiff) obeying Kirchhoff’s law is essential for realizing visual attention, and the smaller Idiff, the better object contour clarity, as evidenced in Fig. S1323,38. Meanwhile, differential signals can suppress common-mode noise and lead to a higher information compression ratio39,40. In addition, spatial-temporal operations (edge enhancement and extraction) of static and dynamic objects are validated by manipulating PPC/NPC characteristics of the device (Figs S14–S17). In this case, the distribution of optimal gate voltages (VgIdiff-min) corresponding to each wavelength with an interval of 10 nm is recorded as the differential current reaches its minima (Idiff-min), as shown in Fig. 2e. It is worth noting that each color equips its corresponding optimal set of gate voltage, e.g., blue (4 and −14 V), green (8 and −32 V) and red (10 and −34 V). By legitimately analyzing the statistics of VgIdiff-min distribution, it is reasonably believed derived operation condition of primary colors can facilitate the subsequent implementation of AVA with adjustable feedback.

Spectral active feedback mechanism
With the aforementioned differential current response for different wavelengths in the volitional neuromorphic devices, the device-level AVA with spectral active feedback is conceptualized. This operation leverages the dynamic modulation capabilities of neuromorphic devices to enhance the accuracy of motion spectra and thus focus on objects. Therefore, the crucial aspect of implementation lies in the reconstruction of the object spectrum. The reconstruction process is briefly outlined in Fig. 3a–c. The single differential photocurrent matrix (Idiff-pc) with different Vg and wavelength is inputted, and each unknown reflection image input yields a reconstructed spectrum after neural network training via gradient descent, explained by the formula below:

Idiff-pc = Γ(Vpositive, Vnegative) Svector(λ) + ν (1)

where Γ(Vpositive, Vnegative) is the spectral response matrix, Svector(λ) is a vector representing the spectrum, which is dependent on the wavelength resolution, and ν denotes noise. By performing a comparative analysis between the reconstructed spectra Svector(λ) and the corresponding reference spectra Svector(λ) within the training dataset, the neural network undergoes optimization through the resolution of Eq. 2:

Svector(λ) = argmin kΓ(Vpc, Vpc) Svector(λ) - Ik2 (2)

During training, a combination of Mean Squared Error and L1 norm was used as the loss function. After only 79 rounds of training, the model ultimately stabilized, minimizing convergence issues and restricting the solution space (Figs. S18, S19)41,42. Utilizing the preferred light source (xenon lamp), simulative reconstruction of different transmission spectra can be obtained in Fig. 3c and Fig. S20. We observed that the spectrum reconstructed from the optimized neural network agrees well with the measured reference spectrum. To further evaluate the model performance, the reconstruction error of the model at various wavelengths is recorded, as presented in Fig. 3d. Across the entire dataset, the model achieved an accuracy rate of 92.2% with a spectral resolution of approximately 0.24 nm.

The accuracy of spectral reconstruction hinges on voltage and the resulting Idiff-pc, and the lower Idiff-pc can enhance the effectiveness of motion recognition. By optimizing the voltage corresponding to Idiff-pc, accuracy can be improved for specific wavelengths, thereby modulating signals from desired motion targets. This approach effectively suppresses non-target spectral signals, highlighting signals from spectral motion target, the corresponding process is illustrated in Fig. 3d–f. To begin, we establish the average reconstruction accuracy as the baseline for each wavelength across all voltage combinations in Fig. 3d. Taking the spectral information at 733 nm as an example, the Vg mapping for this wavelength can be derived (Fig. 3e). By sorting the differential currents, the minimum differential photocurrent and the corresponding optimal set of gate voltages are obtained. Multiple minima of Idiff-pc values are identified, guiding the subsequent spectral reconstruction process for each voltage combination. By subtracting the established average accuracy curve from the accuracy spectrum obtained under the voltage combination corresponding to the minimum Idiff-pc, we reveal the accuracy variation across the spectrum. This comparison aids in assessing whether the wavelength achieves optimal improvement in spectral reconstruction accuracy. As depicted in Fig. 3e, spectral reconstruction accuracy peaks at 733 nm when utilizing the specific voltage combination of (–20, 38 V). This optimal voltage configuration is determined through iterative optimization, reflecting the device’s operation learning process and serving as a crucial condition for subsequent operational “consciousness” formation of the device. Furthermore, the same procedure demonstrates maximum reconstruction accuracy in red, green, and blue light (Fig. 3f), further validating the effectiveness of this method across different wavelengths.

Active target demonstration
The device-level AVA is believed to easily demonstrate its advantages in numbers of scenarios to actively identify the desired target among multiple moving objects. Building upon feature extraction, it can achieve fusion and superposition of features through accumulation and averaging. This operation is beneficial for obtaining trajectories of multispectral moving objects captured in the images. The proof-of-concept demonstration is illustrated in Fig. 4a–c, sustained attention is illustrated for individuals moving in red, green, and blue colors. The 3D plots effectively display each person’s trajectory in a distinct color, with the backdrop of a tiled floor for spatial reference. The figures clearly delineate the paths of the individuals, demonstrating the system’s ability to track them accurately amidst potential distractions. It is evident that the information captured in these images is clear and precise, offering a stark contrast to the data obtained through PAA, which appears cluttered and chaotic (Fig. S21). In addition, the extraction of moving objects and color tracking were demonstrated through single device whiskbroom scanning system in Figs. S22, S23. Figure 4d–f illustrates the recognition accuracy of the device over time at different wavelengths. For all frame rates, the recognition accuracy often exceeds 93%, with small fluctuations and relatively few outliers, indication of reliable recognition performance. The AVA significantly enhances image compression by prioritizing areas with high informational content, thereby optimizing storage and improving the efficiency of data transmission without sacrificing key visual details. Dividing the pixels in the grayscale range of 0–256 into 20 equally spaced statistical units. The spectral reconstruction mechanism achieves high-efficiency data compression by selectively preserving the target object’s characteristic spectral channels. Key principles include: (1) A threshold comparator filters pixels with effective edges in Fig. 4g (lower part). (2) The original image spectrum is compressed to only the color channel of the target object in Fig. 4g (upper part). Static backgrounds and irrelevant moving objects are ignored, which can significantly reduce data volume, ultimately achieving a compression ratio of 1.17%, which outperforms other neuromorphic devices as summarized in Table S4 (Detailed calculations in Supplementary Information). In addition, the AVA mechanism contributes to low energy consumption by selectively processing only relevant information. The information-energy efficiency (IEE) is comprehensively evaluated through the power consumption aspect of human like visual information processing, quantified in joules per bit (J/bit)10,43,44. Utilizing an evaluation method based on the resting state and action potential of human brain neurons (Fig. S24), the IEE of our volitional neuromorphic device can be as low as 0.625 pJ/bit. This efficiency is comparable to the energy consumption of neurons in the human retina, which is approximately 0.714 pJ/bit, and is slightly higher than the energy consumption observed in the neurons of mouse and Drosophila, as shown in Fig. 4h10,45,46. Meanwhile, IEE demonstrates obvious advantages compared to other neuromorphic devices (Table S5). This comparison highlights the potential of the AVA mechanism to approach near-biological levels of energy efficiency in AI systems. Notably, a good device-to-device repeatability has been substantiated via additional validations on vdW heterostructure, device performance and accuracy simulation (Figs. S25–29). By employing AVA, a more comprehensive and refined understanding of the scene was achieved, facilitating effective tracking and analysis of moving objects with multispectral features.

We have successfully demonstrated a sub-picojoule-per-bit volitional neuromorphic device by introducing an AVA. The intervened AVA operation with active feedback and real-time correction, leveraging gate-tunable differential spectral characteristics, enables our device precisely identify and track specific objects with an impressive accuracy over 93%. As a result, the volitional neuromorphic device boasts a data compression ratio of 1.17%, which significantly reduces redundant data and achieves an extreme information energy efficiency of 0.625 pJ/bit. These advancements mark a major breakthrough in neuromorphic engineering, highlighting the potential to redefine the future of AI hardware with brain-like efficiency.

Methods
Materials: Heavily p-doped Si substrates coated with SiO2 layer (90 nm) were purchased from Corning Inc. The MoSe2, h-BN and MoS2 were supplied by Mai Ta Corp. (Nanjing, China). Acetone (IPA, anhydrous, 99.5%), isopropanol (anhydrous, 99.5%), ethanol (anhydrous, 99.9%) were purchased from Aladdin.

Device fabrication: Thoroughly clean the SiO2/Si substrate in acetone, IPA, and ethanol using ultrasonic treatment for 10 min. Use Scotch tape to mechanically exfoliate off MoS2 flakes, h-BN, and MoSe2 flakes, and sequentially transfer them onto a SiO2/Si substrate with PDMS assistance, heating to 75 °C, 65 °C, and 75 °C, respectively, during each transfer. The electrode patterning was carried out by ultraviolet lithography of the MoSe2/h-BN/MoS2. Then, the Cr/Au (10/60 nm) contact pads were deposited by electron beam evaporation, followed by a standard lifted-off process in acetone. In order to improve the ohmic contacts between Au electrodes and MoSe2, the as-fabricated devices were annealed at 300 °C in the argon atmosphere. Furthermore, we have successfully fabricated a 3×3 array device (Figs. S30, S31). Despite slight device-to-device variations (Fig. S32), the essential functional switching mechanism remains consistently robust. Future work will therefore focus on refining heterostructure fabrication process to enhance device-to-device reproducibility and fabrication variability, which represents one of the critical steps for scaling towards intelligent systems.

Material and device characterizations: Raman, PL, and AFM (Atomic Force Microscope) of MoSe2/h-BN/MoS2 were measured by a Raman-atomic force system (Alpha300RA, WITec) under 532 nm excitation laser diode (2 mW). TEM and Energy Dispersive X-Ray Spectroscopy (EDX) of MoSe2/h-BN/MoS2 were represented by Electron microscope Talos F200S and Spectrum SUPER X, respectively. The morphology and elemental mapping were measured by Scanning Electron Microscopy (SEM, ZEISS EV0MA15) and Energy Dispersive Spectrometer (EDS, SDD type 80T), respectively. XPS spectra of heterojunctions were measured using Thermo Scientific K-Alpha with an Alka (hv = 1486.8 eV) emission source. The optoelectronic properties of the photoelectric memory were measured with the SemiProbe probe station and a semiconductor parameter analyzer (Keithley 4200), and Platform Design Automation (PDA, FS-Pro). The lasers of 450, 520 and 635 nm were emitted and controlled by a Programmable DC Power Supply (Itech Electronic, IT6100B), a function generator and an irradiatometer. The combined control of a supercontinuum light source (SuperK Compact) and tunable fiber filter (WLTF-NM-P-1550) is used to emit lasers of different wavelengths and light with varying linewidths. The noise spectral density was measured using a semiconductor parameter analyzer equipped with a noise testing module (FS-Pro, Primarius). All the measurements for devices were operated ambient conditions.

Implementation of ResFCNet: In the AVA simulation framework, the neural network consists of an input layer accepting 16-dimensional feature vectors, followed by a sequence of residual blocks with progressively increasing dimensionality (256, 512, and 1024 units, respectively). Each residual block is composed of multiple dense layers with regularization and ReLU activation functions, with skip connections to preserve gradient flow in deep network configurations. The output layer is designed to produce 1650-dimensional vectors for spectral reconstruction. For a comprehensive guide on how to reproduce the specific results and figures presented in this work, including the model training (Fig. S19) and spectral reconstructions (Fig. 3c–f and Fig. S20), please refer to the Appendix: Implementation and Utilization of the ResFCNet Architecture in the Supplementary Information.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents an approach to address the high energy consumption challenges in AI hardware by introducing human visual system-inspired volitional neuromorphic devices. Leveraging adjustable differential spectral responses through gate voltage manipulation, the proposed devices incorporate an active volitional attention (AVA) mechanism that enhances spectral reconstruction accuracy via neural networks. However, some points are not well explained, here are some concerns that require addressing.\n1. How does the AVA mechanism differ fundamentally from existing passive attention mechanisms? What specific innovations make AVA a novel contribution to neuromorphic device technology?\n2. In what ways does spectral reconstruction contribute to achieving the reported data compression ratio of 1.17%? How does it balance compression with the preservation of essential information for accurate object recognition?\n3. Many reports have studied the devices for motion detection. What are the advantages of the proposed motion detection compared with other reports? The manuscript seems not to provide scenarios in which spectral reconstruction is beneficial.\n4. The neural network training process used for spectral reconstruction like the training methodology, datasets, etc., is suggested to be supplemented.\n5. What are the roles and advantages of NPC and PPC for spectral construction? It seems that the spectral construction doesn’t need the NPC and PPC.\n6. How do the energy efficiency (0.625 pJ/bit) and data compression ratio (1.17%) of the proposed device compare quantitatively to current state-of-the-art neuromorphic devices? Is there a detailed comparative analysis to highlight the relative improvements?", "reviewer_2": "This work presents a study on volitional neuromorphic devices designed to address the critical challenge of energy consumption in computational systems. The authors propose an innovative active volitional attention (AVA) mechanism, by manipulating photoconductance and distinguishing reconstruction accuracy. The devices utilizing the AVA mechanism enabled an impressive data compression ratio of 1.17% and extreme energy efficiency of 0.625 pJ/bit. The authors further demonstrated attractive targeting and tracking of moving and color objects with high accuracy. The work is an innovative and thorough study, which represents a notable achievement for the community, and I recommend its publication in Nature Communications after minor revisions.\nThe AVA proposed in this study is very interesting and attractive. For better readership, I suggest authors to clarify the correlation between the as-fabricated device and human vision system.\nIs PPC and NPC response the foundation of initiating AVA process? It is crucial for authors to explicitly explain the corresponding working mechanism in terms of carrier dynamics.\nWhy did the authors implement dense layers in the neural network? How is this neural network related to the differential spectral signals?\nThe differential photoreponse for the subsequent spectral reconstruction seems relatively weak. Please comment on whether it potentially compromises processing accuracy.\nThere are some typos and labeling errors. For example, the title would be better changed as “Sub-Picojoule-Per-Bit Volitional Neuromorphic Devices for Precise Targeting and Tracking”; Figure S17 and Figure S24 lack labels of a, b, and c; the vertical axis annotation, V_g^(I_(diff-min) ), of the image of Figure 1e should be consistent with that described in the manuscript. The authors should correct these issues for clarity.", "reviewer_3": "Response to Reviewer #1\nReviewer #1 (Remarks to the Author):\nThis manuscript presents an approach to address the high energy consumption challenges in\nAI hardware by introducing human visual system-inspired volitional neuromorphic devices.\nLeveraging adjustable differential spectral responses through gate voltage manipulation, the\nproposed devices incorporate an active volitional attention (AVA) mechanism that enhances\nspectral reconstruction accuracy via neural networks. However, some points are not well\nexplained, here are some concerns that require addressing.\nReviewer #2 (Remarks to the Author):\nThis work presents a study on volitional neuromorphic devices designed to address the critical\nchallenge of energy consumption in computational systems. The authors propose an\ninnovative active volitional attention (AVA) mechanism, by manipulating photoconductance\nand distinguishing reconstruction accuracy. The devices utilizing the AVA mechanism\nenabled an impressive data compression ratio of 1.17% and extreme energy efficiency of\n0.625 pJ/bit. The authors further demonstrated attractive targeting and tracking of moving and\ncolor objects with high accuracy. The work is an innovative and thorough study, which\nrepresents a notable achievement for the community, and I recommend its publication in\nNature Communications after minor revisions.\nReviewer #3 (Remarks to the Author):\nSeveral critical issues need to be addressed before the manuscript can be considered for\nfurther review. The manuscript, as it stands, lacks clarity in several key areas, and additional\nexplanation, results, and clarification are necessary to improve the overall understanding of\nthe work. Based on the current state of the manuscript, I recommend a major revision. A\nfurther decision will be made after the authors address the points raised in the following\nreview response.\nReviewer #1 (Remarks to the Author):\nThis manuscript presents an approach to address the high energy consumption challenges in\nAI hardware by introducing human visual system-inspired volitional neuromorphic devices.\nLeveraging adjustable differential spectral responses through gate voltage manipulation, the\nproposed devices incorporate an active volitional attention (AVA) mechanism that enhances\nspectral reconstruction accuracy via neural networks. However, some points are not well\nexplained, here are some concerns that require addressing."}, "author_response": "Response to Reviewer #1\nReviewer #1 (Remarks to the Author):\nThis manuscript presents an approach to address the high energy consumption challenges in\nAI hardware by introducing human visual system-inspired volitional neuromorphic devices.\nLeveraging adjustable differential spectral responses through gate voltage manipulation, the\nproposed devices incorporate an active volitional attention (AVA) mechanism that enhances\nspectral reconstruction accuracy via neural networks. However, some points are not well\nexplained, here are some concerns that require addressing.\nResponse: We deeply appreciate the positive comments and recognition for our work. The\npoint-by-point responses to the reviewer comments are listed below.\nComment 1: How does the AVA mechanism differ fundamentally from existing passive\nattention mechanisms? What specific innovations make AVA a novel contribution to\nneuromorphic device technology?\nResponse: Thanks for the comment. The fundamental difference between AVA mechanism\nand existing PAA mechanism is the process of active feedback and correction. AVA enables\nvolitional selection meaning that can choose at will from the environment what to select,\nwhile PAA can only highlights on the objects through salient signals (Acta Psychol., 2010,\n135, 77-99).\nThe current neuromorphic device technology lies on PAA, which lacks an active selection\nfunction and consequently may lead to data redundancy. With the employment of AVA by\nactively distinguishing the spectral reconstruction accuracy, our devices can achieve active\nfeedback and lower data compression.\nThe specific innovation lies that AVA can input gate adjustable differential spectral response,\nand realize more efficient AVA adjustment by distinguishing the spectral reconstruction\naccuracy with the help of neural networks, which can effectively reduce redundant data.\nTo clarify the fundamental difference between AVA and existing PAA, the following texts\nhave been updated in the revised manuscript:\nPage 2:\n“As illustrated in Fig. 1a, PAA, naturally attracted by external stimuli, preferentially\ndominates the process of forming ideology directly from significant signal attention.”\nComment 2: In what ways does spectral reconstruction contribute to achieving the reported\ndata compression ratio of 1.17%? How does it balance compression with the preservation of\nessential information for accurate object recognition?\nResponse: Thank you for the question. The data compression ratio of 1.17% is the\nconsequence of embedding AVA that volitionally selects features of objects (i.e. motion and\nspectra). AVA is fundamentally originated from the spectral reconstruction and accuracy\ndistinction, which enables active feedback and correction, and consequently neglects\nredundant sensory data of stationary backgrounds and unattended moving objects.\nAs shown in Fig. R1 (Fig. S16) and Fig. R2 (Fig. 4g), the amount of data required for object\nrecognition has decreased, but the edges and color information of the object we want to\nrecognize are still included. Therefore, although the data volume is compressed, the accuracy\nof the objects we recognize is still preserved.\nFig. R1 (a) The principle of edge extraction preprocessing for motion detection. The result of\nframe difference calculations the moving man image, including only (b) moving detection and\n(c) motion recognition after edge extraction preprocessing.\nFig. R2 The fundamental of data compression after AVA mechanism. The color channel only\nselects objects and discards background data, and the pixel brightness distribution of the\noriginal image and AVA mechanism is normalized.\nTo clearly demonstrate the advantages of achieving a data compression ratio of 1.17% and\nbalancing accuracy through spectral reconstruction, the revised manuscript has been updated\nwith the following text:\nPage 11:\n“Subsequently, by integrating a threshold comparator into the process, it is capable of\nselecting only the pixels that contain effective edges in Fig. 4g (lower part). Meanwhile,\nthe original image spectrum is compressed to only the color channel of target object in\nFig. 4g (upper part). Static backgrounds and irrelevant moving objects are ignored,\nwhich can significantly reduce data volume, ultimately achieving a compression ratio of\n1.17%.”\nComment 3: Many reports have studied the devices for motion detection. What are the\nadvantages of the proposed motion detection compared with other reports? The manuscript\nseems not to provide scenarios in which spectral reconstruction is beneficial.\nResponse: Thanks for reviewer’s constructive suggestions. Previous reported devices\nautomatically focused on moving objects, but lack of further feature selection., namely\npassive detection method. Our device achieves active feedback and correction by optimizing\nthe gate adjustable differential spectral light response, and has selective recognition for\nmoving objects.\nThe beneficial scenarios applicable to the work of this manuscript include multi-sport\nenvironments such as road tracking, biology follows, adaptive cruise control, etc.\nTo highlight the advantages and application scenarios of AVA in this study, the revised\nmanuscript has been updated with the following text:\nPage 3:\n“This results in redundant sensory data and heightened power consumption, especially\nin multi-sport scenarios such as road tracking, biology follows, adaptive cruise control,\netc.1,”\nComment 4: The neural network training process used for spectral reconstruction like the\ntraining methodology, datasets, etc., is suggested to be supplemented.\nResponse: Thank you for your suggestion. The training process of the neural network has\nbeen supplemented in the corresponding content of Fig. S19.\nPage 30 in the Supplementary Information:\n“We have constructed a photocurrent-spectral dataset with 18,400 data sets by testing\nthe devices at different voltages and light conditions and calibrating the spectra of the\nlight conditions by a spectrometer. In the training process, 90% of the data is used for\ntraining and 10% of the data is used as a test set. The model eventually converged after\n79 rounds of training with the Adam optimizer. The training and testing processes were\ncompleted by Python version 3.8 (ubuntu 20.04) and Tensorflow version 2.9.0 on a cloud\ncomputing platform equipped with an NVIDIA RTX 4090 graphical processing unit. It\nusually took six hours for training.”\nComment 5: What are the roles and advantages of NPC and PPC for spectral construction? It\nseems that the spectral construction doesn’t need the NPC and PPC.\nResponse: Thank you for the comment. NPC and PPC are front-loaded for spectral\nreconstruction. In fact, this is similar to the visual information processing process in the\nhuman brain. Motion feature extraction achieved by PAA at the retina and the front end of\nvisual pathway relies on NPC and PPC (similar to cone cells). AVA is actually realized in the\nmore advanced visual pathways. Therefore, we used the differential photocurrent after NPC\nand PPC processing for spectral reconstruction in the device functional implementation. This\nactually not only leads to a higher information compression ratio, but also suppresses\ncommon mode noise. (Adv. Mater. 2406568, 1–9 (2024), Adv. Funct. Mater. 2423360, 1–29\n(2025))\nTo clarify the advantages of differential operations between NPC and PPC for spectral\nreconstruction, the revised manuscript has been updated with the following text:\nPage 4:\n“, which generate both non-volatile positively and negatively photoconductive\nphotocurrent (PPC and NPC) over visible spectrum to achieve polarity regulation\n(similar to cone cells).”\nPage 7:\n“Meanwhile, differential signals can suppress common mode noise and lead to a higher\ninformation compression ratio2,3.”\nComment 6: How do the energy efficiency (0.625 pJ/bit) and data compression ratio (1.17%)\nof the proposed device compare quantitatively to current state-of-the-art neuromorphic\ndevices? Is there a detailed comparative analysis to highlight the relative improvements?\nResponse: Thank you for raising the question. The information-energy efficiency (IEE) of\nhuman-like visual information processing, measured in joules per bit (J/bit), was used to\nthoroughly evaluate our device. Actually, we try our bests to quantitatively compare the\nenergy efficiency and compression ratio of our device with those of current state-of-the-art\nneuromorphic devices. However, due to the absence of neuronal signature features in current\nstate-of-the-art neuromorphic devices, particularly spike-triggered response characteristics of\naction potentials, direct quantitative comparisons with neuroscience-derived IEE metrics\nremain technically unfeasible. A fundamental objective of neuromorphic computing devices\nlies in achieving functional performance at energy consumption levels approaching biological\nneuronal efficiency. To systematically evaluate this capability, we conducted a comparative\nanalysis of device-level IEE against visual neurons from phylogenetically diverse organisms\nwith well-established visual pathways, including humans and model species spanning\nmultiple body size scales (e.g., mouse and Drosophila). Experimental measurements\ndemonstrate that our device exhibits marginally superior energy efficiency compared to\nmouse and dipteran neuronal systems as illustrated in Fig. R3 (Fig. 4h), while remaining\nbelow the metabolic expenditure of human retinal neurons (~0.714 pJ/bit).\nFig. R3 Comparison of information-energy efficiency for different objects: 0.625 pJ/bit of\nvolitional neuromorphic devices (VND), 0.714 J/bit of human brain, 0.574 pJ/bit of mouse\nand 0.329 pJ/bit of drosophila.\nAdditionally, the high data compression ratio is based on our implementation of AVA\noperations, which can selectively detect moving targets while ignoring non-essential static\nand motion information as illustrated in Fig. R2 (Fig. 4g). The comparison here is directly\ncompared with the original information, and the use of this quantitative data is also to reflect\nthe advantages of our equipment.\nResponse to Reviewer #2\nReviewer #2 (Remarks to the Author):\nThis work presents a study on volitional neuromorphic devices designed to address the critical\nchallenge of energy consumption in computational systems. The authors propose an\ninnovative active volitional attention (AVA) mechanism, by manipulating photoconductance\nand distinguishing reconstruction accuracy. The devices utilizing the AVA mechanism\nenabled an impressive data compression ratio of 1.17% and extreme energy efficiency of\n0.625 pJ/bit. The authors further demonstrated attractive targeting and tracking of moving and\ncolor objects with high accuracy. The work is an innovative and thorough study, which\nrepresents a notable achievement for the community, and I recommend its publication in\nNature Communications after minor revisions.\nResponse: We were grateful for the commendable remarks and acknowledgment of our\nefforts. The point-by-point responses to the reviewer comments are listed below.\nComment 1: The AVA proposed in this study is very interesting and attractive. For better\nreadership, I suggest authors to clarify the correlation between the as-fabricated device and\nhuman vision system.\nResponse: Thank you for suggestion. As shown in Fig. R4 (Fig. 1a-b), the architecture\nemulates the operational principles of the fovea centralis in human visual system through gate\nvoltage feedback. The implementation of gate voltage feedback emulates neuromorphic signal\ntransduction mechanisms in retinal cone photoreceptors, establishing a biologically inspired\nsignal processing framework that preserves spectral reconstruction fidelity through voltage\nfeedback.\nFig. R4 Schematic representation for (a) active volitional attention (AVA) modulation of and\n(b) the proposed volitional neuromorphic devices. Three key operations are included, incident\nlight receiving for photoelectric conversion, neural network training for spectral\nreconstruction, and a feedback mechanism for error calibration.\nTo clearly demonstrate the correlation between device and human vision system, the revised\nmanuscript has been updated with the following text:\nPage 4:\n“As the aforementioned AVA in human visual system (Fig. 1a), the fovea reflects the\npolarity peak change of the cone photoreceptor, ensuring the fundamental information\npre-processing. When an attention is focused on a region of interest, the corresponding\nneuronal potential activity is immediately enhanced and feeds back to the cone\nphotoreceptors, promoting selective task perception and cognition. Similarly, as shown\nin Fig. 1b, the device-level AVA is proposed by introducing an active spectral feedback\ncirculation, which progressively manipulates voltage feedback until outputs optimal\nspectral reconstruction accuracy.”\nComment 2: Is PPC and NPC response the foundation of initiating AVA process? It is\ncrucial for authors to explicitly explain the corresponding working mechanism in terms of\ncarrier dynamics.\nResponse: Thank you for raising the question. Yes, PPC and NPC response is the foundation\nof initiating AVA process. The necessary condition for starting AVA is to optimize the gate-\ntunable differential spectral light response, which is directly depended on PPC and NPC. The\ncorresponding carrier dynamics are shown in Fig. R5 (Fig. S12). Upon the cessation of the\ncontrol negative gate voltage, the MoS layer experiences hole trapping concomitant with the\n2\naccumulation of electrons within the MoSe conduction channel. Once under illumination,\n2\nphotogenerated electrons within MoSe undergo transfer to MoS through Fowler-Nordheim\n2 2\ntunneling. Subsequently, these electrons recombine with the trapped holes within MoS ,\n2\ninducing a weakened control over the floating gate within the MoS conducting channel. This\n2\nattenuated reverse shot effect manifests in diminished electron accumulation within MoSe ,\n2\nresulting in NPC and vice versa to PPC.\nFig. R5 Positive and negative photoconductive mechanisms in 2D volitional neuromorphic\ndevices.\nThe specific principles and Fig. R5 have been updated in the revised Supplementary\nInformation.\nPage 17-18 in the Supplementary Information:\n“The band alignment plays a critical role in charge transport and trapping dynamics for\nproposed heterostructure devices. Specifically, MoS₂, MoSe and h-BN serve as floating\n2\ngate control layer, conductive channel layer and insulating barrier layer, respectively.\nUnder zero gate bias, MoSe (with a bandgap of ~1.4 eV) exhibits intrinsic n-type\n2\nbehavior due to selenium vacancies, while h-BN (a wide-bandgap semiconductor, ~5.76\neV) forms an electron-blocking barrier4,5. The valence band (VB) of h-BN aligns higher\nthan that of MoSe , creating a reverse-biased potential at h-BN/MoSe interface that\n2 2\nprevents vertical charge leakage. When the negative control gate voltage (-V ) is applied,\ng\nelectrons accumulate in the MoSe channel and tunnel via Fowler-Nordheim\n2\nmechanisms through the triangular potential barriers formed by h-BN layers into\nlocalized defect states in the MoS₂ floating gate. This process stores negative charges in\nthe MoS₂, modulating the electric field across the MoSe . Upon illumination,\n2\nphotogenerated electron-hole pairs are generated in the MoSe , and the electrons in\n2\nremain confined in MoSe because the h-BN-induced band bending reverses the surface\n2\npotential. Recombination of these tunneling electrons with trapped holes in MoS₂\nreduces the net negative charge stored, weakening the electric field control over the\nMoSe channel. This attenuation results in diminished electron accumulation under\n2\nreverse bias (negative photoconductance, NPC), while forward illumination enhances\nhole accumulation (positive photoconductance, PPC) through the same mechanism. The\ncritical dependence on band alignment, defect density in MoS₂, and h-BN barrier\nthickness governs the charge storage kinetics and photoresponse characteristics of the\ndevice.”\nComment 3: Why did the authors implement dense layers in the neural network? How is this\nneural network related to the differential spectral signals?\nResponse: Thanks for the comment. The dense layer is a fundamental component of neural\nnetworks, and is primarily used to map input features to high-dimensional space through\nlinear transformations (weight matrix multiplication + bias), and then combined with\nactivation functions to introduce nonlinear capabilities, enabling the model learn complex\ndata relationships.\nIn spectral reconstruction tasks, differential photocurrent signals are used as inputs for neural\nnetworks. Through the nonlinear mapping capability of multi-layer perceptrons (MLPs),\nhighly abstract and underdetermined signals (with low input dimensions or sparse\ninformation) are transformed into high-dimensional and complete spectral information\n(output).\nTo clarify the role of dense layers and elucidate the relationship between neural networks and\ndifferential spectral signals, the following text has been added.\nPage 28-29 in the Supplementary Information:\n“Dense layer is the core component of Multilayer Perceptron (MLP), which is one of the\nmost basic deep neural network structures. The MLP consists only of alternating stacks\nof fully connected layers (linear transformation) and activation functions (nonlinear\ntransformation), and has an intuitive structure. Importantly, according to the Universal\nApproximation Theorem, even an MLP with only one hidden layer can approximate any\ncontinuous function with a sufficient number of neurons6. This allows MLP to\ntheoretically solve nonlinear problems of any complexity. By stacking multiple hidden\nlayers, MLP can learn higher-order nonlinear relationships in the data and are suitable\nfor a variety of tasks such as classification and regression. MLP is still the cornerstone\nmodel for machine learning by virtue of its simplicity, versatility and flexibility.\nAlthough it is gradually replaced by CNN, Transformer, etc. in complex tasks, it is still\nirreplaceable in structured data modelling, lightweight scenarios and theoretical\nresearch7,8.\nIn spectral reconstruction, actually the differential photocurrent signal is the input of\nour designed neural network and the output is the reconstructed spectral information.\nIn our spectral reconstruction task, we carried out an improved design of the MLP to\naddress the high level of abstraction of the differential spectral signal information and\nthe underdetermination of the problem. The fully connected layer can be upscaled or\ndownscaled by adjusting the number of output neurons, which also facilitates us to solve\nthe underdetermined problem of spectral reconstruction. According to Universal\nApproximation Theorem, a deeper stacked network is utilized to cope with the high\ncomplexity and large data volume of spectral reconstruction.”\nComment 4: The differential photoreponse for the subsequent spectral reconstruction seems\nrelatively weak. Please comment on whether it potentially compromises processing accuracy.\nResponse: Thank you for raising the question. The weak differential photocurrent does not\ncompromise spectral reconstruction accuracy, as this accuracy is primarily determined by the\ntraining robustness of neural networks rather than the absolute magnitude of input signals.\nNotably, smaller differential photocurrents enhance system sensitivity to subtle variations,\nenabling precise detection of fine edge features in moving objects. Conversely, larger\ndifferential currents risk saturating signal dynamics, leading to loss of critical details (e.g.,\nblurred boundaries and amplified background noise) that degrade recognition resolution.\nThus, minimized differential currents inherently optimize device operation by preserving high\nsignal-to-noise ratios while maintaining the fidelity required for neural network-driven\nspectral reconstruction.\nComment 5: There are some typos and labeling errors. For example, the title would be better\nchanged as “Sub-Picojoule-Per-Bit Volitional Neuromorphic Devices for Precise Targeting\nand Tracking”; Figure S17 and Figure S24 lack labels of a, b, and c; the vertical axis\nannotation, V_g^(I_(diff-min) ), of the image of Figure 1e should be consistent with that\ndescribed in the manuscript. The authors should correct these issues for clarity.\nResponse: Thank you for raising the question. We have carefully checked the entire\nmanuscript and made corresponding corrections.\nPage 1:\n“Sub-Picojoule-Per-Bit Volitional Neuromorphic Devices for Precise Targeting and\nTracking”\nFig. R6 (Fig. S17) and Fig. R7 (Fig. S24):\nFig. R6 The original image and normalized pixel brightness distribution. The pixel\nbrightness distribution after motion detection with and without moving objects, including\nalso two modes for moving detection and motion recognition after edge extraction\npreprocessing.\nFig. R7 The energy consumption of MoS /h-BN/MoSe device: (a) constant light power\n2 2\nas a function of duration light. (b) constant duration of light as a function of light power.\n(c) constant duration light and light power as a function of gate voltage.\nPage 6:\n“In this case, the distribution of optimal gate voltages (𝑽\n𝐈𝒅𝒊𝒇𝒇−𝒎𝒊𝒏) \ncorresponding to each\ng\nwavelength with…”\nResponse to Reviewer #3\nReviewer #3 (Remarks to the Author):\nSeveral critical issues need to be addressed before the manuscript can be considered for\nfurther review. The manuscript, as it stands, lacks clarity in several key areas, and additional\nexplanation, results, and clarification are necessary to improve the overall understanding of\nthe work. Based on the current state of the manuscript, I recommend a major revision. A\nfurther decision will be made after the authors address the points raised in the following\nreview response.\nResponse: We greatly appreciate positive feedback and suggestions on our work. The\nfollowing is a point-by-point response to the reviewer's comments.\nComment 1: Organization of the Manuscript: The manuscript appears more like an array\nsimulation in real-time, yet the actual case involves the fabrication of a single phototransistor\nwith a floating gate. The large-scale production, repeatability, and performance of such a\ndevice will pose significant challenges, as highlighted in the manuscript's claim: \"As a proof\nof concept, we use the MoSe₂/h-BN/MoS₂ single device to receive each pixel image one by\none to perform optical images and preprocess them for edge detection by programming their\nphotocurrent valve for the voltage.\" However, there is no information on device-to-device\nverification, repeatability, or performance across multiple devices. The manuscript includes\noptimization techniques and extensive mathematical calculations to determine values and\nparameters, but there is insufficient experimental validation. This omission is a significant\ndrawback. To address these gaps, the supplementary information (device SEM, device\nfabrication, XRD, XPS, AFM data, etc.) should be moved to the main manuscript.\nAdditionally, the mechanism behind the photoresponse and the floating gate charge\ntrapping mechanism should be explained in more detail, with the necessary band\ninformation included to enhance the clarity of the main ideas.\nResponse: Thanks for the constructive suggestions. For a single device, we have conducted\ncomprehensive material characterizations (including TEM, EDS, XPS, AFM, Raman, etc.)\nand photoelectical measurements (I-V characteristics, light pulse responses, electrical pulse\nstimulation, and photoelectric coupling tests) to thoroughly validate its performance.\nFollowing your suggestions, we have incorporated TEM, EDS, XPS and Raman analysis in\nthe revised manuscript, as shown in Fig. R8 (Fig. 1): The TEM and EDS results explicitly\ndemonstrate the well-defined interfacial structure between materials, while the XPS data\nconfirm the van der Waals stacking configuration between different material layers. Raman\nspectroscopy provides conclusive evidence for the successful construction of the\nheterojunction.\nFig. R8 (a) The TEM image and EDX mapping for the vertical cross-section of MoSe /h-\n2 2 2 2\nheterostructure. (c) Resolved XPS spectra of Mo, Se, S, B and N core levels.\nThe corresponding contents and Fig. R9 have been updated in the revised manuscript.\nPage 4:\n“, we proposed volitional neuromorphic devices with extreme energy efficiency by\nsimulating the fovea response to regions of interest, configured with a van der Waals\nheterostructure of MoSe /h-BN/MoS (Fig. S1). The structural, interfacial,\n2 2\nmorphological and elementary characteristics are evaluated by conducting transmission\nelectron microscope (TEM), Raman, X-ray photoelectron spectroscopy (XPS), atomic\nforce microscope (AFM) and energy dispersive X-ray spectroscopy (EDS), respectively.\nAs shown in Fig. 1c, clear interfaces and hierarchical element distribution are observed\nin the vdWs heterostructure. The corresponding characteristic E (383 cm-1, 233 cm-1\n2g\nand 1350 cm-1) and A (408 cm-1) peaks are identified for few-layer MoS flakes (as\n1g 2\nfloating gate), MoSe flakes (as conduction channel) and h-BN flakes (as potential\n2\nbarrier), respectively (Fig. 1d)9–11. To further evaluate the interfacial characteristics, the\nresolved XPS spectra of Mo 4f, Se 3d, S 2p, B 1s and N 1s core levels is analyzed in Fig.\n1e9–11, and the symmetric peak profiles of h-BN suggests that vdWs interactions\npredominantly govern the interfacial characteristics12. In addition, the morphology\nprofile and element distribution are recorded in Fig S2 and Fig. S3, further confirming\nthe successful fabrication of proposed heterostucture with floating gate.”\nFig. R9 Schematic representation for (a) active volitional attention (AVA) modulation of and\n(b) the proposed volitional neuromorphic devices. Three key operations are included, incident\nlight receiving for photoelectric conversion, neural network training for spectral\nreconstruction, and a feedback mechanism for error calibration. (c) The\nTEM image and EDX mapping for the vertical cross-section of MoSe /h-BN/MoS\n2 2\nheterostructure. (d) The Raman spectra of MoS , h-BN and MoSe /h-BN/MoS\n2 2 2\nheterostructure. (e) Resolved XPS of Mo, Se, S, B and N core levels.\nThe extraction of moving object and color tracking were additionally demonstrated through\nsingle device whiskbroom scanning system (Fig. R10 and Fig. R11), which have been\naccordingly addressed in the revised Supplementary Information (Fig. S22 and S23).\nFig. R10 The results of edge extraction of different colored moving objects based on a single\nvolitional neuromorphic device.\nFig. R11 Simulate the AVA mechanism to recognize and select target moving objects based\non single AVA device: (a) red, (b) green and (c) blue moving object and motion trajectories.\nSubsequently, we performed more detailed analysis of the photoresponse characteristics and\nfloating-gate charge trapping mechanisms based on the band structure information, which\nsignificantly strengthens the physical interpretation of the device operation principles.\nThe corresponding explanation has been updated:\nPage 17-18 in the Supplementary Information:\n“The band alignment plays a critical role in charge transport and trapping dynamics for\nproposed heterostructure devices. Specifically, MoS₂, MoSe and h-BN serve as floating\n2\ngate control layer, conductive channel layer and insulating barrier layer, respectively.\nUnder zero gate bias, MoSe (with a bandgap of ~1.4 eV) exhibits intrinsic n-type\n2\nbehavior due to selenium vacancies, while h-BN (a wide-bandgap semiconductor, ~5.76\neV) forms an electron-blocking barrier4,5. The valence band (VB) of h-BN aligns higher\nthan that of MoSe , creating a reverse-biased potential at h-BN/MoSe interface that\n2 2\nprevents vertical charge leakage. When the negative control gate voltage (-V ) is applied,\ng\nelectrons accumulate in the MoSe channel and tunnel via Fowler-Nordheim\n2\nmechanisms through the triangular potential barriers formed by h-BN layers into\nlocalized defect states in the MoS₂ floating gate. This process stores negative charges in\nthe MoS₂, modulating the electric field across the MoSe . Upon illumination,\n2\nphotogenerated electron-hole pairs are generated in the MoSe , and the electrons in\n2\nremain confined in MoSe because the h-BN-induced band bending reverses the surface\n2\npotential. Recombination of these tunneling electrons with trapped holes in MoS₂\nreduces the net negative charge stored, weakening the electric field control over the\nMoSe channel. This attenuation results in diminished electron accumulation under\n2\nreverse bias (negative photoconductance, NPC), while forward illumination enhances\nhole accumulation (positive photoconductance, PPC) through the same mechanism. The\ncritical dependence on band alignment, defect density in MoS₂, and h-BN barrier\nthickness governs the charge storage kinetics and photoresponse characteristics of the\ndevice.”\nComment 2: Interface Information: There is no clear information regarding the interface\nformed between MoSe₂/h-BN/MoS₂. To clarify the interface's role and behavior, I recommend\nincluding XPS characterization data, which would provide insight into the van der Waals\nforces and interfacial interactions. The current characterization techniques, such as XRD\nand cross-sectional analysis, mainly reflect the crystalline behavior and do not provide\nsufficient details about the interface formation. This crucial aspect should be addressed with\nproper data and explanation.\nResponse: Thanks for the constructive suggestions. We have performed XPS to characterize\nthe interface behaviour of MoSe /h-BN/MoS heterostructure. The resolved XPS spectra of\n2 2\nMo 4f, Se 3d, S 2p, B 1s and N 1s core levels is recorded and analyzed. Particularly, the\nsymmetrical peak profile of h-BN as a barrier layer indicates that the vdW interaction mainly\ncontrols the interface characteristics. Meanwhile, we have additionally conducted TEM and\nEDS (Fig. R8), and the interfaces can be clearly observed.\nTo emphasize the interface formation of proposed heterostructure, the corresponding contents\nand Fig. R8 have been updated in the revised manuscript. The modified content is consistent\nwith Comment 1.\nComment 3: Inconsistent Light Power Intensity: The manuscript mentions an incident light\npower of 0.1 nW in the main text, but Figure S5 presents data with a much higher intensity\nrange, from 0.1 mW to 5 mW. This discrepancy between the reported light power in the\nmanuscript and the data in the figures is concerning. The authors must clarify this\ninconsistency and ensure that the results align with the reported experimental conditions.\nResponse: Thank you for pointing this out. The incident power mentioned in the manuscript\nshould be 0.1 mW. We have carefully revised manuscript and made corresponding\ncorrections.\nPage 5:\n“, accompanied by exceptional responsivity, detectivity and noise-equivalent-power with\nlow incident light power of 0.1 mW (Fig. S4–S6).”\nComment 4: Lack of Explanation on PPC and NPC Behavior: The manuscript claims that\nboth PPC and NPC exhibit good uniformity and reproducibility, with a rise time of less than\n160 μs (Fig. S8). However, there is no clear explanation of how V_GS biasing, light pulses,\nand V_DS are synchronized during the switching of these mechanisms. It is unclear how the\nphotoresponse varies with bias conditions. The authors should provide a detailed explanation\nof how PPC and NPC behave under different biasing conditions (V_GS, V_DS) and how this\ninfluences the photoresponse. Furthermore, the current experimental setup, as shown in\nFigure S9, does not provide valuable information regarding the three-terminal floating gate\nphototransistor. I suggest performing more detailed experiments under specific bias\nconditions to better characterize the behavior of the photoresponse.\nResponse: Thank you for your suggestion. The explanation of how V biasing, light pulses,\nGS\nand V are synchronized during the switching of these mechanisms has been added. The\nDS\nsynchronous switching of the three parameters is precisely controlled by the computer,\nespecially the switching between V pulses and optical pulses. In addition, we demonstrate\nGS\nthat PPC and NPC are modulated by V (gate bias) and V (drain bias) through the\nGS DS\nfollowing mechanisms:\n• Gate Bias (V ): Removal of negative V induces band bending in MoS₂, creating\nGS GS\nhole-trapping localized states. Concurrently, electron accumulation in MoSe₂ forms a\nhighly conductive channel.\n• Drain Bias (V ): Higher V strengthens the vertical electric field at the MoSe /h-\nDS DS 2\nBN/MoS interface. This enhances Fowler-Nordheim tunneling, reducing the\n2\ntunneling barrier and boosting photogenerated electron transfer from MoSe to MoS .\n2 2\nAccelerated electron-hole recombination neutralizes space charges in MoS₂,\nweakening its electrostatic screening on MoSe . Reduced carrier density in MoSe ’s\n2 2\nconduction band lowers channel conductivity under illumination, amplifying NPC.\nSimilar voltage-dependent tunneling and recombination dynamics govern PPC. Thus,\nlarger V directly amplifies photoresponse magnitude (PPC/NPC), while V\nDS GS\ncontrols the initial carrier distribution and trapping states. (Nat. Nanotechnol. 17, 27–\n32 (2022), ACS Nano 12, 9513–9520 (2018))\nThis interplay between bias-induced band engineering and tunneling efficiency explains the\ntunable photoresponse in such heterostructures.\nTo address the issues from reviewer, we have updated appropriate explanation and extra\nmeasurements in the revised manuscript and Supplementary Information.\nPage 6:\n“A single V pulse is initially applied for 1 s, and the light is simultaneously switched on\ng\nonce disconnecting the gate voltage.”\nPage 13-14 in the Supplementary Information:\n“Fig. S10 shows the modulation effect of the drain voltage on the PPC and NPC, which\ndemonstrates that larger drain voltages can cause larger response photocurrents. Upon\nthe removal of the negative gate bias, the MoS layer undergoes band bending-induced\n2\nhole trapping (forming localized states), while electron accumulation is established in\nthe MoSe conductive channel via carrier injection, rendering the channel in a highly\n2\nconductive state. Under elevated source-drain voltages, an enhanced vertical electric\nfield develops at the MoSe₂/h-BN/MoS₂ heterostructure interface. The intensified\nelectric field strength effectively reduces the tunneling barrier height through Fowler-\nNordheim (F-N) tunneling mechanisms, thereby significantly enhancing the transfer\nefficiency of photogenerated electrons from the conduction band of MoSe to MoS 13,14.\n2 2\nThis accelerated electron-hole recombination process neutralizes the space charges\nwithin MoS , diminishing its electrostatic screening effect on the MoSe channel.\n2 2\nConsequently, the suppressed carrier density in the conduction band of MoSe manifests\n2\nas a pronounced reduction in channel conductivity, ultimately leading to the observed\nlower current state under illumination. This mechanism underpins the voltage-\ndependent enhancement of the NPC effect in such heterostructured systems. Similar to\nthe PPC effect.”\nAs Fig. S9 did not clearly show the complete floating gate effect, we have measured the\noutput curves as a function of gate voltage (Fig. R12), which can well illustrate the three\nterminal floating gate phototransistors. The corresponding context and Fig. R12 have been\nupdated in the revised manuscript.\nPage 11 in the Supplementary Information:\n“Fig. S8(c) shows the output curve of the device under electrical pulses by sweeping the\nbias voltage from 0 to 2V. It is obvious that the gate voltage is adjusted to different\nresistance states.\nFig. R12 Output transfer curves as applying gate voltage from -40 to 40 V.”\nPage 6-7:\n“As shown in Fig. 2d, PPC clearly transforms to NPC as tuning the direction of in-\nvertical electric field by switching V from positive to negative, which is consistent with\ng\naforementioned device physics. Notably, both PPC and NPC possess gradual response to\nboth distinctive wavelength and gate voltage.\nFig. R13 Photocurrent mapping as a function of gate voltage within the spectrum\nranging from 450 to 800 nm. The gate voltage ranges from -40 to 40 V with a step size of\nComment 5: Issues with Figures and Plotting:\n1, Figure S11a: This figure is difficult to interpret, and the voltage sweeps and memory\nvoltage window are not distinguishable. Using different colors for each sweep voltage will\nimprove clarity and help differentiate the various conditions.\nResponse: Thank you for your suggestion. We have updated Fig. R14 with different colors\nfor each memory window.\nPage 16 in the Supplementary Information:\n“Fig. R14 Transfer characteristics at V of 1 V for different scanning gate voltages.”\nds\n2, Figure S11c: The manuscript claims that a positive V_GS pulse results in a higher current\nresponse, while a negative V_GS pulse yields a much lower current. A detailed explanation\nis required to clarify whether this behavior is due to accumulation, depletion, or inversion.\nThe authors should provide a more thorough discussion of these mechanisms.\nResponse: Thank you for your suggestion. The gate voltage modulated current response can\nbe originated from the carrier accumulation/depletion dynamics and interface trapping effects,\nthe explanation is concluded as below:\n• Positive V pulse to electron accumulation: A positive V drives electrons back\nGS GS\nfrom MoS₂ to MoSe₂ via tunneling, accumulating electrons in the MoSe₂ channel.\nThis enhances conductivity, resulting in a higher current state under drain bias.\n• Negative V pulse to hole trapping & depletion: Negative V induces hole trapping\nGS GS\nin MoS₂, creating localized states that deplete electrons in MoSe₂. Even after\nremoving V , residual holes in MoS₂ maintain a depleted MoSe₂ channel, locking\nGS\nthe system in a lower current state.\nThe following discussion of above mechanisms has been updated.\nPage 16 in the Supplementary Information:\n“When V is swept towards positive, electrons are driven back from MoS to MoSe\ng 2 2\nthrough tunneling, and the current increases under the drive of the drain bias.\nMeanwhile, when the gate voltage is removed, due to the hole trapping effect of MoS , it\n2\nultimately remains in a lower current state than the initial current state. Subsequently,\nduring the erasing operation, the trapping effect at the MoS interface is relatively\n2\nstrong, causing the electrons in the MoSe channel to be depleted. After removing the\n2\nnegative gate voltage, the current level continues to increase compared to when the gate\nvoltage is applied under the mutual restriction of residual hole trapping centers and\ninterface tunneling effects.”\nComment 6: Non-Volatile Memory Behavior: The claim of non-volatile memory behavior is\nnot well substantiated in the manuscript. The memory retention data presented in Figure S9c\nand S9d is insufficient, and there is no clear correlation between the weight change of the\nmemory device and the applied light and voltage. The authors should address the following:\n1. Provide more detailed data on the memory retention at discrete levels.\nResponse: Thank you for your suggestion. We conducted a more detailed analysis of the\nimpact of sustained pulses on the device results, as shown in Fig. R15.\nFig. R15 The pulse width and interval of incident light are programmed as 200 ms and\n1.5 s, respectively. The linear variation of (a) PPC and (b) NPC with the number of\npulses.\n2. Discuss how the photoresponse is combined with light and voltage to determine weight\nchange, and explain how the device's retention characteristics affect the weight levels.\nResponse: Thank you for your suggestion. In conventional edge computing paradigms, each\nkernel matrix typically requires distinct weights tailored to the photoelectric responsivity,\nwhich diverges from our approach. In our study, spectral reconstruction accuracy is\nfundamentally governed by the resolution of differential photocurrents. The differential\nphotocurrent originates from device-level operations executed at nodes, adhering to\nKirchhoff's law, and arises from non-volatile positive and negative photoconductive\n(NPC/PPC) photocurrents generated by two devices operating under opposite gate voltages.\nConsequently, the weight modulation determined by the combined optical and electrical\nstimuli corresponds to variations in differential currents, which are intrinsically linked to the\ninterplay between PPC and NPC behaviors. The differential photocurrent employed in our\nframework critically determines the efficacy of motion feature extraction. As demonstrated in\nFig. R16, background noise amplifies with increasing differential current, resulting in\ndegraded reconstruction fidelity of object contours. Specifically, elevated differential currents\nimpair recognition accuracy and precision by erasing subtle features and merging object\nboundaries into noisy backgrounds. This underscores the imperative of maintaining low\ndifferential currents to optimize edge detection and motion feature extraction.\nFig. R16 Image edge reconstruction effect corresponding to the different differential currents.\n(a) Original image. (b) Reconstructed image of I =2.68x10-10. (c) Reconstructed image of\ndiff-pc\nI =1.19x10-8. (d) Reconstructed image of I =2.29x10-7.\ndiff-pc diff-pc\nIn addition, for the AVA architecture, the maintenance level of memory effect does not\ndirectly determine the weight levels of system, but in high-speed motion scenes (<160 μs\n\nresponse), the carriers retained by Fowler Nordheim tunneling provide continuous inter frame\nsignal coherence for real-time differential computation (PPC/NPC) of neural networks,\navoiding target trajectory breakage caused by photoelectric response delay.\nThe active will regulation of AVA embeds the core logic of motion recognition into the\ndynamic weight update of neural networks, while the memory characteristics only serve as\nthe signal preprocessing module of physical hardware. Its non-ideality can be compensated by\nthe photoelectric current normalization at the algorithm end, achieving system level fault\ntolerance. The biomimetic advantage of AVA lies in transforming the attention mechanism\nfrom traditional \"memory dependent\" reconstruction (such as frame caching) to \"event\ndriven\" processing, where the influence weights are covered by the adaptive learning ability\nof neural networks.\n3. Perform experiments to demonstrate the weight linearity and retention at multiple levels,\nand clarify how many error-free weight levels can be achieved with high retention.\nResponse: Thank you for your suggestion. As shown in Fig. R15, we have increased the\nnumber of pulses to 25 and observed significant nonlinear changes. This can be attributed to\nthe saturation of hole trap states in MoS , resulting in a decrease in the charge increment\n2\ninduced by unit pulses and a nonlinear response.\n• Linear Regime (Pulse Count < 15):\nHigh-fidelity discrete levels: Photocurrent modulation shows near-perfect linearity\n(R² > 0.97), as each pulse fills unoccupied hole traps in MoS₂.\nStable retention: Sequential pulses induce quantized photocurrent steps (~ΔI/step),\nenabling reliable multi-level memory encoding.\n• Nonlinear Regime (Pulse Count > 15):\nTrap-state saturation: Finite trap density (~15-pulse capacity) limits recombination\nsites, reducing incremental ΔI per pulse.\nDegraded discreteness: Post-saturation, photocurrent steps diminish nonlinearly,\ncompromising discrete-level resolution.\nThe corresponding explanation and Fig. R15 (Fig. S9) have been updated.\nPage 12 in the Supplementary Information:\n“The non-volatile photocurrent between the PPC and NPC was quantified across a\nrange of optical pulse counts by extracting the step photocurrent in Fig. S9(b, c). As\nevidenced by the linear fitting R-squared values exceeding 0.97 for both non-volatile\nphotocurrents when the pulse count is below 15, the excellent linearity between optical\npulse number and photocurrent is confirmed. However, when exceeding 15 pulses, the\nresponse gradually transitions to nonlinearity, which can be attributed to the finite\ndensity of trapped hole states in the MoS layer. During the initial phase (pulse count <\n2\n15), photoexcited electrons from each optical pulse effectively occupy unoccupied trap\nstates, enabling a linear correlation between photocurrent modulation and pulse\naccumulation. As the trap states approach saturation (pulse count > 15), the available\nrecombination sites diminish significantly. Subsequent electrons encounter reduced\nopportunities to recombine with trapped holes, thereby weakening the floating-gate\ncharge control efficiency per pulse.”\nComment 7: Weight Scheme and Device Performance: The manuscript lacks clear\ninformation on how the weight scheme is read. The authors should provide a detailed\nexplanation of how the device performance is evaluated in terms of weight change,\nparticularly in the context of light exposure and voltage application.\nResponse: Thank you for your suggestion. This variation in weighting is attributed to the\nmagnitude of differential current, and the differential photocurrent employed in our task\ndictates the effectiveness of motion feature extraction.\nFig. R16 illustrates that background noise intensifies with the increase of differential current,\nresulting in a less distinct reconstruction of object contours. Specifically, a higher differential\ncurrent diminishes the accuracy and precision of the recognition process by erasing small\nfeatures and blending the boundaries of objects into the noisy background. This underscores\nthe importance of small differential currents to enhance edge detection and motion feature\nextraction.\nTo provide a better readership, the operation of weight scheme has been added in revised\nmanuscript and Supplementary Information.\nPage 7:\n“A single Vg pulse is initially applied for 1 s, and the light is simultaneously switched on\nonce disconnecting the gate voltage.”\nPage 19 in the Supplementary Information:\n“The impact of differential photocurrent on background noise and object recognition\nclarity is clarified. As shown in Fig. S13(a-c), the background noise becomes more\npronounced when the differential current increases, leading to a less distinct\nreconstruction of the object's contours. Noteworthily, larger differential currents result\nin the loss of fine details, blurring object boundaries and noisy background, thereby\ndiminishing the accuracy and resolution of the recognition process.”\nComment 8: Response to Voltage Sweep (Figure S11a): The authors should explore how the\nmemory window would change when considering a voltage sweep from -40V to 40V.\nAdditionally, the manuscript should explain how the memory window is affected by light\nexposure (red, blue, and green) in both the presence and absence of light.\nResponse: Thank you for your suggestion. We have performed corresponding measurements\nof memory windows to confirm the impacts from gate voltage and illumination, as shown in\nFig. R17 (Fig. S11).\nFig. R17 (a) Transfer characteristics at V of 1 V for different scanning gate voltages. (b) The\nds\nrelationship between ΔV and V . (c) Transfer characteristics at V of 1 V under red, green,\ng-max ds\nand blue light illumination. (d) The retention performance of memory after write and erase\noperations.\nUnder red, green, and blue light illumination, the memory window of the floating-gate\ntransistor remains stable, but the stored charge increases significantly. This is explained by\nphoton-assisted tunneling and band modulation. Photons at different wavelengths do not\ndirectly affect charge tunneling or storage barrier height, maintaining memory stability.\nHowever, they excite carriers in the channel or interfacial regions, enabling additional charge\ninjection into the floating gate and increasing stored charge density. Furthermore, photo-\ninduced interface states or defects may provide extra charge trapping sites. Despite this, the\nmemory window remains unchanged, indicating that optical fields optimize charge storage\ncapacity without altering the device's inherent storage barrier structure.\nPage 15 in the Supplementary Information:\n“As shown in Fig. S11(c), under red, green, and blue light illumination, the memory\nwindow of the floating-gate transistor exhibits no significant variation, while the stored\ncharge quantity demonstrates a marked increase. Under optical excitation, photon\nenergies may fall below the bandgap of either the tunneling layer or floating-gate\nmaterial, thereby preventing direct photon-induced charge tunneling or modification of\nstorage barrier height, which maintains memory window stability. However,\nphotogenerated carriers (electron-hole pairs) can be excited in the channel or interfacial\nregions, enhancing charge injection efficiency under electric field driving forces. This\nfacilitates increased electron capture into the floating gate through the tunneling layer,\nleading to elevated stored charge density. This observation indicates that optical fields\ncan indirectly optimize charge storage capacity by modulating carrier concentration\nwithout altering the inherent storage barrier structure of the device.”\nComment 9: Calculation of Optimal Gate Voltage in Figures 2D and 2E: There is no clear\nexplanation provided for how the optimal gate voltage in Figure 2D (\"Photocurrent mapping\nas a function of gate voltage within the spectrum ranging from 450 to 800 nm, with the gate\nvoltage ranging from -40 to 40 V in steps of 2 V and a bias voltage of 1 V\") and Figure 2E\n(\"The histogram of the optimal gate voltage corresponding to the minimum differential\nphotoresponse, with each color representing its corresponding wavelength\") is calculated.\nThese explanations are essential to clarify how these values are determined and how they\nare used for further simulations. The authors should provide a clear and detailed\nmethodology for how the optimal gate voltages (V_G^(diff-min)) are calculated and how\nthese values influence the subsequent simulations and results.\nResponse: Thank you for your suggestion and we are sorry for the confusion. Actually, the\noptimal gate voltage (Fig. 2e) is originated from selection of the minimum differential\nphotocurrent (the difference of NPC and PPC, measured as a function of gate voltage within\nthe spectral range of 450-800 nm, Fig. 2d). To evaluate the optimal gate voltage combination,\nwe iterate over all positive and negative gate voltage combinations and use Kirchhoff's law to\ncalculate the corresponding differential current. By sorting the differential currents, the\nminimum differential photocurrent and the corresponding optimal set of gate voltages are\nobtained. In fact, there are some very close values of the minimum differential photocurrent,\nwe present only one of the optimal grid voltages (the voltages corresponding to the upper and\nlower edges of the color block are a set of optimal gate voltages).\nNotably, the measured NPC and PPC is the necessary basis for the implementation of\ndifferential operation on the device. The variation of the spectral response of the device\noriginated from gate voltage is obvious, therefore it is beneficial for the subsequent spectral\nreconstruction.\nThe spatial-domain differential operations (edge enhancement and extraction) and temporal-\ndomain differential operations (motion feature extraction) that we performed using the PPC\nand NPC functions of the device are shown in Fig. R18 and R19 (Fig. S14 and Fig, S15 in the\nSupplementary Information), respectively. The differential photocurrent actually originates\nfrom the device-level operations performed at the nodes following Kirchhoff's law by the\nnon-volatile positively and negatively photoconductive photocurrent generated by two\ndevices operating at positive and negative gate voltages.\nFig. R18 The retinomorphic vision sensor based on a vdW vertical heterostructure for\nsimultaneous image sensing and processing: (a) Edge extraction principle: The image is\ngrayscale encoded, and after interval on/off processing, differential operation is performed.\nThese operations are realized by controlling the photoresponse of each pixel in the sensor by\nvarying V independently (b) Demonstration of image processing with three different\ng\noperations: 1, adjacent columns; 2, adjacent rows; 3, interval inverted; Photo credits: Logo of\nOptoelectrical Detection and Sensor group.\nFig. R19 The mechanism of motion recognition based differential operation for positive (P )\nmxn\nand negative (N ) conductivity in volitional neuromorphic devices, and a demonstration of\nmxn\nthe original image and the recognized image.\nOur purpose of choosing the most suitable voltage is to get the clearest effect of motion\nfeature extraction, which extracts high-frequency information, i.e., information that changes\ndrastically, from the global information. Therefore, the differential method of calculating the\ncalculating the\nchange values is widely used to extract high-frequency information. On this basis, the\ndifferential photocurrent used in our task determines the effect of motion feature extraction.\nFig. R16 (Fig. S13 in the Supplementary Information) shows that the background noise\nintensifies with increasing differential current, resulting in a less clear reconstruction of the\nobject's outlines. This emphasises how crucial it is to reduce differential current in order to\nenhance edge detection and motion feature extraction.\nIn order to better help readers understand this process, we have also made corresponding\nmodifications in the revised manuscript.\nPage 7：\n“As mentioned earlier, differential current (I ) obeying the Kirchhoff's law is essential\ndiff\nfor realizing visual attention, and the smaller I , the better the object contour clarity,\ndiff\nas evidenced in Fig. S13.”\n“In addition, spatial-temporal operations (edge enhancement and extraction) of static\nand dynamic object are validated by manipulating PPC/NPC characteristics of the\ndevice (Figs. S14-S17).”\nPage 9:\n“Taking the spectral information at 733 nm as an example, the V mapping for this\ng\nwavelength can be derived (Fig 3e). By sorting the differential currents, the minimum\ndifferential photocurrent and the corresponding optimal set of gate voltages are\nobtained. Multiple minima of I values are identified, guiding the subsequent\ndiff-pc\nspectral reconstruction process for each voltage combination.”\nAdditional information\nResponse: We have further polished the Abstract. In addition, all the changes made in the\nrevised manuscript leave no impact on our conclusion.\n“The technological revolution driven by artificial intelligence (AI) has significantly\nimproved the hardware performance, but energy consumption remains a critical\nbottleneck. The state-of-the-art retinomorphic devices, as core components of AI\nhardware, excel in feature extraction but are constrained by passive attention\nmechanisms that lack flexibility of actively extracting additional features. Inspired by\nthe human visual system, this work introduces a volitional neuromorphic device with\nactive volitional attention (AVA) regulation. By leveraging gate-voltage-tunable\nphotoconductance to generate adjustable differential spectral response and employing\nneural networks to evaluate spectral reconstruction accuracy, the device achieves\nselective task perception. Experimental results demonstrate a data compression ratio of\n1.17% and an extreme information energy efficiency of 0.625 pJ/bit. This advancement\nnot only advances retinomorphic hardware design but also provides a sustainable\npathway for energy-efficient hyperspectral imaging and next-generation neuromorphic\ncomputing systems.”\nReference\n1. Jiménez-Bravo, D. M., Lozano Murciego, Á., Sales Mendes, A., Sánchez San Blás, H.\n& Bajo, J. Multi-object tracking in traffic environments: A systematic literature\nreview. Neurocomputing 494, 43–55 (2022).\n2. Ng, S. E. et al. Retinomorphic Color Perception Based on Opponent Process Enabled\nby Perovskite Bipolar Photodetectors. Adv. Mater. 2406568, 1–9 (2024).\n3. Han, J. et al. 2D Materials-Based Photodetectors with Bi-Directional Responses in\nEnabling Intelligent Optical Sensing. Adv. Funct. Mater. 2423360, 1–29 (2025).\n4. Cassabois, G., Valvin, P. & Gil, B. semiconductor. 10, (2016).\n5. Huang, Y. et al. High-Performance Broadband Visible−Near Infrared Photodetector\nEnabled by Atomic Capping Layer. Adv. Opt. Mater. 10, 1–10 (2022).\n6. Function, S. Mathematics of Control , Signals , and Systems Approximation by\nSuperpositions of a Sigmoidal Function * yrx a ( t )__, fl unit. 303–314 (1989).\n7. Tolstikhin, I. et al. MLP-Mixer: An all-MLP Architecture for Vision. Adv. Neural Inf.\nProcess. Syst. 29, 24261–24272 (2021).\n8. Liu, R., Li, Y., Tao, L., Liang, D. & Zheng, H. T. Are we ready for a new paradigm\nshift? A survey on visual deep MLP. Patterns 3, (2022).\n9. Chen, X. et al. Fabry-Perot interference and piezo-phototronic effect enhanced flexible\nMoS2 photodetector. Nano Res. 15, 4395–4402 (2022).\n10. Pan, S. et al. Light trapping enhanced broadband photodetection and imaging based on\nMoSe2/pyramid Si vdW heterojunction. Nano Res. 16, 10552–10558 (2023).\n11. Fukamachi, S. et al. Large-area synthesis and transfer of multilayer hexagonal boron\nnitride for enhanced graphene device arrays. Nat. Electron. 6, 126–136 (2023).\n12. Lin, Y. C. et al. Low Energy Implantation into Transition-Metal Dichalcogenide\nMonolayers to Form Janus Structures. ACS Nano 14, 3896–3896 (2020).\n13. Zhang, Z. et al. All-in-one two-dimensional retinomorphic hardware device for motion\ndetection and recognition. Nat. Nanotechnol. 17, 27–32 (2022).\n14. Wang, Y. et al. Negative Photoconductance in van der Waals Heterostructure-Based\nFloating Gate Phototransistor. ACS Nano 12, 9513–9520 (2018)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have provided a greatly revised manuscript. My concerns have been well addressed. Now I recommend it for publishing.", "reviewer_2": "All my comments have been well addressed in the revised manuscript, i have no further question and happy to recommend it for publication.", "reviewer_3": "Author failed to answer first part of my question 1 which is clear limitation of this work:\nThe manuscript appears more like an array simulation in real-time, yet the actual case involves the fabrication of a single phototransistor with a floating gate. The large-scale production, repeatability, and performance of such a device will pose significant challenges, as highlighted in the manuscript's claim: \"As a proof of concept, we use the MoSe2/h-BN/MoS2 single device to receive each pixel image one by one to perform optical images and preprocess them for edge detection by programming their photocurrent valve for the voltage.\" However, there is no information on device-to-device verification, repeatability, or performance across multiple devices.\nAuthor should supplement the code for review and upload the code on github for fair publication process and provide code in supplementary zip file\nAlso developed code complete execution protocol of simulator should be provided step bt step\nAlso provide developed simulator and upload on github and supplement complete zip file as supplementary file"}, "author_response": "Response to Reviewer #1\nReviewer #1 (Remarks to the Author):\nThis manuscript presents an approach to address the high energy consumption challenges in\nAI hardware by introducing human visual system-inspired volitional neuromorphic devices.\nLeveraging adjustable differential spectral responses through gate voltage manipulation, the\nproposed devices incorporate an active volitional attention (AVA) mechanism that enhances\nspectral reconstruction accuracy via neural networks. However, some points are not well\nexplained, here are some concerns that require addressing.\nResponse: We deeply appreciate the positive comments and recognition for our work. The\npoint-by-point responses to the reviewer comments are listed below.\nComment 1: How does the AVA mechanism differ fundamentally from existing passive\nattention mechanisms? What specific innovations make AVA a novel contribution to\nneuromorphic device technology?\nResponse: Thanks for the comment. The fundamental difference between AVA mechanism\nand existing PAA mechanism is the process of active feedback and correction. AVA enables\nvolitional selection meaning that can choose at will from the environment what to select,\nwhile PAA can only highlights on the objects through salient signals1.\nThe current neuromorphic device technology lies on PAA, which lacks an active selection\nfunction and consequently may lead to data redundancy. With the employment of AVA by\nactively distinguishing the spectral reconstruction accuracy, our devices can achieve active\nfeedback and better data compression.\nThe specific innovation lies that AVA can input gate adjustable differential spectral response,\nand realize more efficient AVA adjustment by distinguishing the spectral reconstruction\naccuracy with the help of neural networks, which can effectively reduce redundant data.\nTo clarify the fundamental difference between AVA and existing PAA, the following texts\nhave been updated in the revised manuscript:\nPage 2:\n“As illustrated in Fig. 1a, PAA, naturally attracted by external stimuli, preferentially\ndominates the process of forming ideology directly from significant signal attention.”\n“The AVA is further intervened as the subjective selecting command is sent to the\nbrain's central thinking region guided by prior knowledge and objectives. Accordingly,\nvisual attention is directed through priority maps to efficiently focus on key information\nwhile ignoring irrelevant stimuli, which facilitates selective feature extraction and thus\nan extreme energy efficiency.”\nThe corresponding innovations of this study have been pronounced in the revised manuscript:\nPage 1-2:\n“By leveraging gate-voltage-tunable photoconductance to generate adjustable\ndifferential spectral response and employing neural networks to evaluate spectral\nreconstruction accuracy, the device achieves selective task perception. Experimental\nresults demonstrate a data compression ratio of 1.17% and an extreme information\nenergy efficiency of 0.625 pJ/bit.”\nComment 2: In what ways does spectral reconstruction contribute to achieving the reported\ndata compression ratio of 1.17%? How does it balance compression with the preservation of\nessential information for accurate object recognition?\nResponse: Thank you for the question. The data compression ratio of 1.17% is the\nconsequence of embedding AVA that volitionally selects features of objects (i.e. motion and\nspectra). Specifically, based on the spectral reconstruction and accuracy distinction, AVA\nthus enables active feedback and correction, and neglects redundant sensory data of stationary\nbackgrounds and unattended moving objects.\nThe balance between compression and preservation of essential information is critical for\naccurate object recognition. As shown in Fig. R1 (Fig. S16) and Fig. R2 (Fig. 4g), although\nthe amount of data required for object recognition has decreased, the featured information of\nedges and color we want to recognize are preserved. Notably, only the redundant data of\nstationary backgrounds and unattended moving objects have been neglected. Therefore,\nalthough the data volume is compressed, the accuracy of object recognition is still high.\nFig. R1 (a) The principle of edge extraction preprocessing for motion detection. The result of frame\ndifference calculations the moving man image, including only (b) moving detection and (c) motion\nrecognition after edge extraction preprocessing.\nFig. R2 The fundamental of data compression after AVA mechanism. The color channel only selects\nobjects and discards background data, and the pixel brightness distribution of the original image and\nAVA mechanism is normalized.\nTo clarify the contribution of spectral reconstruction to the data compression, and the balance\nbetween compression and the preservation of essential information, the revised manuscript\nhas been updated with the following text:\nPage 11:\n“The spectral reconstruction mechanism achieves high-efficiency data compression by\nselectively preserving the target object's characteristic spectral channels. Key principles\ninclude: 1) A threshold comparator filters pixels with effective edges in Fig. 4g (lower\npart). 2) The original image spectrum is compressed to only the color channel of target\nobject in Fig. 4g (upper part). Static backgrounds and irrelevant moving objects are\nignored, which can significantly reduce data volume, ultimately achieving a compression\nratio of 1.17%, which outperforms other neuromorphic devices as summarized in Table\nS4 (Detailed calculations in Supplementary Information).”\nComment 3: Many reports have studied the devices for motion detection. What are the\nadvantages of the proposed motion detection compared with other reports? The manuscript\nseems not to provide scenarios in which spectral reconstruction is beneficial.\nResponse: Thanks for reviewer’s constructive suggestions. Previous reported devices\nautomatically focused on moving objects, namely passive detection method, but lack of\nfurther feature selection. Our device achieves active feedback and correction by optimizing\nthe gate adjustable differential spectral light response, and has selective recognition for\nmoving objects (i.e. spectra). Accordingly, our device can deliver a superior compression\nratio over others. The comparative analysis of motion detection capabilities across reported\nneuromorphic devices is summarized in Table R1 (Table S4). Despite the slightly different\nfunction, our devices demonstrate the best compression ratio.\nYes, providing specific scenarios is important. Benefited from spectral reconstruction, our\ndevices with distinctive spectral features can be implemented into road tracking, biology\nfollows, adaptive cruise control, and etc.\nTable R1. The compression ratio and functions of different neuromorphic devices.\nCompression\nDevices Functions implemented Ref\nratio\nCsPbBr ~50% X-ray intelligent imaging 2\n2 3 2\nBiOBr nanosheets 19.4% Classification recognition 4\nEdge detection and chromatic\nFAPbBr /MAPbI 33% 5\n3 3\ncancellation\nWSe /P(VDF-TrFE) ~80.42% Object motion detection 6\n2 2\nTo highlight the advantages and application scenarios of AVA in this study, the revised\nmanuscript has been updated with the following text:\nPage 3:\n“This results in redundant sensory data and heightened power consumption, especially\nin multi-sport scenarios such as road tracking, biology follows, adaptive cruise control,\netc.7,”\nPage 11:\n“Static backgrounds and irrelevant moving objects are ignored, which can significantly\nreduce data volume, ultimately achieving a compression ratio of 1.17%, which\noutperforms other neuromorphic devices as summarized in Table S4 (Detailed\ncalculations in Supplementary Information).”\nComment 4: The neural network training process used for spectral reconstruction like the\ntraining methodology, datasets, etc., is suggested to be supplemented.\nResponse: Thank you for your suggestion. The training process of the neural network has\nbeen supplemented in the corresponding content of Fig. S19.\nPage 30 in the Supplementary Information:\n“We have constructed a photocurrent-spectral dataset with 18,400 data sets by testing\nthe devices as a function of voltages and light conditions. The spectra of light conditions\nis calibrated by the commercial spectrometer. In the training process, 90% of the data is\nused for training and 10% of the data is used as a test set. The model eventually\nconverged after 79 rounds of training with the Adam optimizer. The training and testing\nprocesses were completed by Python version 3.8 (ubuntu 20.04) and Tensorflow version\n2.9.0 on a cloud computing platform equipped with an NVIDIA RTX 4090 graphical\nprocessing unit. It usually took six hours for training.”\nComment 5: What are the roles and advantages of NPC and PPC for spectral construction? It\nseems that the spectral construction doesn’t need the NPC and PPC.\nResponse: Thank you for the comment. Yes, the spectral construction generally does not\nneed the NPC and PPC. In stark contrast to conventional spectral construction, our study\nemphasizes on the multi-feature extraction, including motion and spectra, thus extreme\nenergy efficiency. Similar to cone cells, NPC and PPC are initially responsible for motion\ndetection but are lack of extra feature extraction (i.e. spectra). To realize a more advanced\nvisual pathways, we further manipulate input signals from NPC and PPC, namely differential\nphotocurrent, as the front-loaded for spectral reconstruction, this actually not only leads to a\nhigher information compression ratio, but also suppresses common mode noise5,8.\nTo clarify the advantages of differential operations between NPC and PPC for spectral\nreconstruction, the revised manuscript has been updated with the following text:\nPage 4-5:\n“The differential operation between NPC and PPC photoconductive currents enables\npolarity-regulated spectral reconstruction by synergistically suppressing interference\nand amplifying target signals.”\n“This can maintain 93% spectral recognition accuracy even under 1.17% data\ncompression by dynamically balancing noise suppression and feature retention.”\nPage 7:\n“Meanwhile, differential signals can suppress common mode noise and lead to a higher\ninformation compression ratio5,8.”\nComment 6: How do the energy efficiency (0.625 pJ/bit) and data compression ratio (1.17%)\nof the proposed device compare quantitatively to current state-of-the-art neuromorphic\ndevices? Is there a detailed comparative analysis to highlight the relative improvements?\nResponse: Thank you for the comment. The information-energy efficiency (IEE) is evaluated\nby considering a series of 600μstime bins (approximately corresponding to the refractory\nperiod of the device) and calculated the information contained in this train, denoted as\n𝐼𝑛 :\n𝑖𝑛𝑝𝑢𝑡\n𝐼𝑛 = −𝑝𝑙𝑜𝑔 (𝑝)−(1−𝑝)𝑙𝑜𝑔 (1−𝑝) (R-1)\n𝑖𝑛𝑝𝑢𝑡 2 2\n, where p is the probability of an action potential occurring in any given time bin, and 𝐼𝑛\n𝑖𝑛𝑝𝑢𝑡\nis given in bits per time bin9. We then calculated the mutual information per time bin\n𝐼𝑛 , which represents how much the sequence of output signals measured in the device\n𝑚𝑢𝑡𝑢𝑎𝑙\n(𝑆 ) tells us about the train of input signals (𝑆 ):\n𝑜𝑢𝑡 𝑖𝑛\n𝐼𝑛 (𝑆 ; 𝑆 ) = 𝐼𝑛 +∑𝑝(𝐼𝑛𝑝𝑢𝑡)∑𝑝(𝐼𝑛𝑝𝑢𝑡|𝑂𝑢𝑡𝑝𝑢𝑡)𝑙𝑜𝑔 [𝑝(𝐼𝑛𝑝𝑢𝑡|𝑂𝑢𝑡𝑝𝑢𝑡)]\n𝑚𝑢𝑡𝑢𝑎𝑙 𝑜𝑢𝑡 𝑖𝑛 𝑖𝑛𝑝𝑢𝑡 2\n(R-2)\nIn this equation, Input equals 1 when there is an input signal and 0 otherwise, and Output\nequals 1 when there is an output signal and 0 otherwise. The sums are over all possible Input\nand Output. Probability distributions were constructed for each recording session for each of\nthe four possible occurrences (Input-Output): (1) an output signal preceded by an input signal\n(1-1); (2) an output signal not preceded by an input signal (0-1); (3) no output signal when\nthere was an input signal (1-0); and (4) no output signal when there was no input signal (0-0).\nThe results of this calculation were divided by the time bin (600μs) to obtain information\nrates in bits per second:\nR = \n𝐼𝑛𝑚𝑢𝑡𝑢𝑎𝑙 (𝑆𝑜𝑢𝑡; 𝑆𝑖𝑛 )\n(R-\nτ\n3)\nTherefore, the information-energy efficiency (IEE) was comprehensively evaluated in terms\nof the power consumption aspect of human-like visual information processing, measured in\njoules per bit (J/bit). IEE is calculated as below:\n𝜂 = \n𝑊𝑑𝑒𝑣𝑖𝑐𝑒\n(R-4)\n𝐼𝐸𝐸\n𝑅\nPrecomputed parameters include: In P , P , P , P , and In- values. However, state-\n-input 11 10 01 00 mutual\nof-the-art neuromorphic devices lack standardized assessment criteria. Therefore, we evaluate\nIEE based on their reported experimental data, as summarized in Table R2. Clearly, our\ndevice exhibits superior IEE over others. (P refer to P(in=1|out=1), other similar)\nneuromorphic devices.\nNeuromorphic Energy\nIn- P P P P In IEE (pJ/bit) Ref\ninput 11 10 01 00 -mutual\ndevices Consumption\nNbS /MoS 0.89 0.73 0.13 0.24 0.87 0.50 0.42 pJ 17.1 22\n2 2\ngraphene/MoS 0.72 0.17 0.20 0.67 0.80 0.82 50 nJ 6.09×105 23\nBP/Al 2 O 3 /WSe 2 /h- 6.42×10-4 4.00×10-5 4.00×10-5 1.00 1.00 7.00×10-4 1.44 nJ 2.06×106 18\nBN\nTiN O /MoS 0.0575 0.00658 6.62×10-3 0.99 0.99 0.0665 450 nJ 6.76×107 25\nx 2-x 2\nNeuromorphic Energy\nIn- P P P P In IEE (pJ/bit) Ref\ninput 11 10 01 00 -mutual\ndevices Consumption\nMoS /Te 0.0351 3.68×10-3 3.69×10-3 0.993 0.996 0.0403 8.5 μJ 2.11×108 26\nMonolayer MoS 0.52 0.57 3.32×10-3 0.43 0.99 0.50 44 nJ 8.84×105 28\n2\n0.93 0.86 0.45 0.0759 0.55 0.52\nMoSe /h-BN/MoS 0.53 nJ 0.625 Work\n2 2\nIn terms of compression ratio, we evaluate it using a two-stage multiplicative model. First, the\nspatial compression factor p is defined as the ratio of pixels retained after motion detection to\n1\nthe original pixel count (e.g., motion-triggered regions versus full-frame data). Second, the\nspectral compression factor p is derived from the ratio of the spectral channel resolution (10\n2\nnm) to the total spectral range (450-850 nm), i.e., p =channel resolution/spectral range. The\n2\noverall compression ratio is then computed as p ×p . The detailed comparative analysis is in\n1 2\nTable R4, demonstrating the best compression ratio.\nTable R3 The compression ratio and functions implemented of different neuromorphic devices.\nCompression\nDevices Functions implemented Ref\nratio\nCsPbBr ~50% X-ray intelligent imaging 2\n2 3 2\nBiOBr nanosheets 19.4% Classification recognition 4\nEdge detection and chromatic\nFAPbBr /MAPbI 33% 5\n3 3\ncancellation\nWSe /P(VDF-TrFE) ~80.42% Object motion detection 6\n2 2\nTo highlight the improvements, Table R2-R3 and following context have been added in the\nrevised manuscript and Supplementary Information:\nPage 11:\n“Meanwhile, IEE demonstrates obvious advantages compared to other neuromorphic\ndevices (Table S5).”\nPage 11:\n“Static backgrounds and irrelevant moving objects are ignored, which can significantly\nreduce data volume, ultimately achieving a compression ratio of 1.17%, which\noutperforms other neuromorphic devices as summarized in Table S4 (Detailed\ncalculations in Supplementary Information).”\nPage 37 in the Supplementary Information:\n“The compression ratio is calculated using a two-stage multiplicative model. First, the\nspatial compression factor p1 is defined as the ratio of pixels retained after motion\ndetection to the original pixel count (e.g., motion-triggered regions versus full-frame\ndata). Second, the spectral compression factor p is derived from the ratio of the spectral\n2\nchannel resolution (10 nm) to the total spectral range (450-850 nm), i.e., p =channel\n2\nresolution/spectral range. The overall compression ratio is then computed as p ×p .\n1 2\nCompared with other neuromorphic devices, as shown in Table S4, it shows dominant\ndata compression.”\nResponse to Reviewer #2\nReviewer #2 (Remarks to the Author):\nThis work presents a study on volitional neuromorphic devices designed to address the critical\nchallenge of energy consumption in computational systems. The authors propose an\ninnovative active volitional attention (AVA) mechanism, by manipulating photoconductance\nand distinguishing reconstruction accuracy. The devices utilizing the AVA mechanism\nenabled an impressive data compression ratio of 1.17% and extreme energy efficiency of\n0.625 pJ/bit. The authors further demonstrated attractive targeting and tracking of moving and\ncolor objects with high accuracy. The work is an innovative and thorough study, which\nrepresents a notable achievement for the community, and I recommend its publication in\nNature Communications after minor revisions.\nResponse: We were grateful for the commendable remarks and acknowledgment of our\nefforts. The point-by-point responses to the reviewer comments are listed below.\nComment 1: The AVA proposed in this study is very interesting and attractive. For better\nreadership, I suggest authors to clarify the correlation between the as-fabricated device and\nhuman vision system.\nResponse: Thank you for suggestion. The as-fabricated device directly mimics three bio-\nvisual mechanisms of the human fovea:\n1) Active volitional attention: Like neuronal potential enhancement during foveal focus, our\nactive spectral feedback loop dynamically amplifies target spectral channels (e.g., blue\nwavelengths), as illustrated in Fig. R3 (Fig. 1a-b).\n2) Synapse-like processing:The MoSe /h-BN/MoS heterostructure replicates retinal synaptic\n2 2\nfunctions, where PPC and NPC mirror cone/rod cells' polarity-opponent signal encoding.\n3) Energy prioritization:Similar to the brain's resource allocation for attended objects, the\ndevice achieves 93% recognition accuracy at 1.17% data compression by selectively\nprocessing only task-relevant spectral features (e.g., a runner's blue shirt), discarding 98.83%\nredundant data through bio-inspired voltage gating.\nFig. R3 Schematic representation for (a) active volitional attention (AVA) modulation of and (b) the\nproposed volitional neuromorphic devices. Three key operations are included, incident light receiving\nfor photoelectric conversion, neural network training for spectral reconstruction, and a feedback\nmechanism for error calibration.\nTo clearly demonstrate the correlation between device and human vision system, the revised\nmanuscript has been updated with the following text:\nPage 4:\n“As the aforementioned AVA in human visual system (Fig. 1a), the fovea reflects the\npolarity peak change of the cone photoreceptor, ensuring the fundamental information\npre-processing. When an attention is focused on a region of interest, the corresponding\nneuronal potential activity is immediately enhanced and feeds back to the cone\nphotoreceptors, promoting selective task perception and cognition. Similarly, as shown\nin Fig. 1b, the device-level AVA is proposed by introducing an active spectral feedback\ncirculation, which progressively manipulates voltage feedback until outputs optimal\nspectral reconstruction accuracy.”\nComment 2: Is PPC and NPC response the foundation of initiating AVA process? It is\ncrucial for authors to explicitly explain the corresponding working mechanism in terms of\ncarrier dynamics.\nResponse: Thank you for raising the question. Yes, PPC and NPC response is the foundation\nof initiating AVA process. The necessary condition for starting AVA is to optimize the gate-\ntunable differential spectral light response, which is directly depended on PPC and NPC. The\ncorresponding carrier dynamics are shown in Fig. R4 (Fig. S12). Upon the cessation of the\ncontrol negative gate voltage, the MoS layer experiences hole trapping concomitant with the\n2\naccumulation of electrons within the MoSe conduction channel. Once under illumination,\n2\nphotogenerated electrons within MoSe undergo transfer to MoS through Fowler-Nordheim\n2 2\ntunneling. Subsequently, these electrons recombine with the trapped holes within MoS ,\n2\ninducing a weakened control over the floating gate within the MoS conducting channel. This\n2\nattenuated reverse shot effect manifests in diminished electron accumulation within MoSe ,\n2\nresulting in NPC and vice versa to PPC.\nFig. R4 Positive and negative photoconductive mechanisms in 2D volitional neuromorphic devices.\nThe specific principles and Fig. R4 have been updated in the revised Supplementary\nInformation.\nPage 17-18 in the Supplementary Information:\n“The band alignment plays a critical role in charge transport and trapping dynamics for\nproposed heterostructure devices. Specifically, MoS , MoSe and h-BN serve as floating\n2 2\ngate control layer, conductive channel layer and insulating barrier layer, respectively.\nUnder zero gate bias, MoSe (with a bandgap of ~1.4 eV) exhibits intrinsic n-type\n2\nbehavior due to selenium vacancies, while h-BN (a wide-bandgap semiconductor, ~5.76\neV) forms an electron-blocking barrier18,19. The valence band (VB) of h-BN aligns higher\nthan that of MoSe , creating a reverse-biased potential at h-BN/MoSe interface that\n2 2\nprevents vertical charge leakage. When the negative control gate voltage (-V ) is applied,\ng\nholes are accumulated in the MoS . After removing the control gate voltage, holes are\n2\ntrapped in the MoS layer and electrons are simultaneously accumulated in the MoSe\n2 2\nconduction channel. Upon illumination, photogenerated electron-hole pairs are\ngenerated in the MoSe , and the electrons in MoSe tend to tunnel through the h-BN\n2 2\nbarrier into the MoS₂ region. Meanwhile, holes remain confined in MoSe because the h-\ntunneling electrons with trapped holes in MoS , weakening the electric field control over\n2\nthe MoSe channel. This attenuation results in diminished electron accumulation under\n2\nreverse bias (negative photoconductance, NPC), while forward illumination enhances\nhole accumulation (positive photoconductance, PPC) through the same mechanism. The\ncritical dependence on band alignment, defect density in MoS , and h-BN barrier\n2\nthickness governs the charge storage kinetics and photoresponse characteristics of the\ndevice.”\nComment 3: Why did the authors implement dense layers in the neural network? How is this\nneural network related to the differential spectral signals?\nResponse: Thanks for the comment. The dense layer is a fundamental component of neural\nnetworks, and is primarily used to map input features to high-dimensional space through\nlinear transformations (weight matrix multiplication + bias), and then combined with\nactivation functions to introduce nonlinear capabilities, enabling the model learn complex\ndata relationships.\nIn spectral reconstruction tasks, differential photocurrent signals are used as inputs for neural\nnetworks. Through the nonlinear mapping capability of multi-layer perceptrons (MLPs),\nhighly abstract and underdetermined signals (with low input dimensions or sparse\ninformation) are transformed into high-dimensional and complete spectral information\n(output).\nTo clarify the role of dense layers and elucidate the relationship between neural networks and\ndifferential spectral signals, the following text has been added.\nPage 28-29 in the Supplementary Information:\n“Dense layer is the core component of Multilayer Perceptron (MLP), which is one of the\nmost basic deep neural network structures. The MLP consists only of alternating stacks\nof fully connected layers (linear transformation) and activation functions (nonlinear\ntransformation), and has an intuitive structure. Importantly, according to the Universal\nApproximation Theorem, even an MLP with only one hidden layer can approximate any\ncontinuous function with a sufficient number of neurons20. This allows MLP to\ntheoretically solve nonlinear problems of any complexity. By stacking multiple hidden\nlayers, MLP can learn higher-order nonlinear relationships in the data and are suitable\nfor a variety of tasks such as classification and regression. MLP is still the cornerstone\nmodel for machine learning by virtue of its simplicity, versatility and flexibility.\nAlthough it is gradually replaced by CNN, Transformer, etc. in complex tasks, it is still\nirreplaceable in structured data modelling, lightweight scenarios and theoretical\nresearch21,22.\nIn spectral reconstruction, actually the differential photocurrent signal is the input of\nour designed neural network and the output is the reconstructed spectral information.\nIn our spectral reconstruction task, we carried out an improved design of the MLP to\naddress the high level of abstraction of the differential spectral signal information and\nthe underdetermination of the problem. The fully connected layer can be upscaled or\ndownscaled by adjusting the number of output neurons, which also facilitates us to solve\nthe underdetermined problem of spectral reconstruction. According to Universal\nApproximation Theorem, a deeper stacked network is utilized to cope with the high\ncomplexity and large data volume of spectral reconstruction.”\nComment 4: The differential photoreponse for the subsequent spectral reconstruction seems\nrelatively weak. Please comment on whether it potentially compromises processing accuracy.\nResponse: Thank you for the comment. The weak differential photocurrent does not\ncompromise spectral reconstruction accuracy, as this accuracy is primarily determined by the\ntraining robustness of neural networks rather than the absolute magnitude of input signals.\nNotably, smaller differential photocurrents enhance system sensitivity to subtle variations,\nenabling precise detection of fine edge features in moving objects. Conversely, larger\ndifferential currents risk saturating signal dynamics, leading to loss of critical details (e.g.,\nblurred boundaries and amplified background noise) that degrade recognition resolution.\nThus, minimized differential currents inherently optimize device operation by preserving high\nsignal-to-noise ratios while maintaining the fidelity required for neural network-driven\nspectral reconstruction.\nComment 5: There are some typos and labeling errors. For example, the title would be better\nchanged as “Sub-Picojoule-Per-Bit Volitional Neuromorphic Devices for Precise Targeting\nand Tracking”; Figure S17 and Figure S24 lack labels of a, b, and c; the vertical axis\nannotation, V_g^(I_(diff-min) ), of the image of Figure 1e should be consistent with that\ndescribed in the manuscript. The authors should correct these issues for clarity.\nResponse: Thank you for raising the question. We have carefully checked the entire\nmanuscript and made corresponding corrections.\nPage 1:\n“Sub-Picojoule-Per-Bit Volitional Neuromorphic Devices for Precise Targeting and\nTracking”\nFig. R5 (Fig. S17) and Fig. R6 (Fig. S24):\nFig. R5 The original image and normalized pixel brightness distribution. The pixel brightness\ndistribution after motion detection with and without moving objects, including also two modes for\nmoving detection and motion recognition after edge extraction preprocessing.\nFig. R6 The energy consumption of MoS /h-BN/MoSe device. Evaluated under (a) constant light\n2 2\npower as a function of duration light, (b) constant duration of light as a function of light power, and (c)\nconstant duration light and light power as a function of gate voltage.\nPage 6:\n“In this case, the distribution of optimal gate voltages (𝑽\n𝐈𝒅𝒊𝒇𝒇−𝒎𝒊𝒏) \ncorresponding to each\ng\nwavelength with…”\nResponse to Reviewer #3\nReviewer #3 (Remarks to the Author):\nSeveral critical issues need to be addressed before the manuscript can be considered for\nfurther review. The manuscript, as it stands, lacks clarity in several key areas, and additional\nexplanation, results, and clarification are necessary to improve the overall understanding of\nthe work. Based on the current state of the manuscript, I recommend a major revision. A\nfurther decision will be made after the authors address the points raised in the following\nreview response.\nResponse: We greatly appreciate positive feedback and suggestions on our work. The\nfollowing is a point-by-point response to the reviewer's comments.\nComment 1: Organization of the Manuscript: The manuscript appears more like an array\nsimulation in real-time, yet the actual case involves the fabrication of a single phototransistor\nwith a floating gate. The large-scale production, repeatability, and performance of such a\ndevice will pose significant challenges, as highlighted in the manuscript's claim: \"As a proof\nof concept, we use the MoSe₂/h-BN/MoS₂ single device to receive each pixel image one by\none to perform optical images and preprocess them for edge detection by programming their\nphotocurrent valve for the voltage.\" However, there is no information on device-to-device\nverification, repeatability, or performance across multiple devices. The manuscript includes\noptimization techniques and extensive mathematical calculations to determine values and\nparameters, but there is insufficient experimental validation. This omission is a significant\ndrawback. To address these gaps, the supplementary information (device SEM, device\nfabrication, XRD, XPS, AFM data, etc.) should be moved to the main manuscript.\nAdditionally, the mechanism behind the photoresponse and the floating gate charge\ntrapping mechanism should be explained in more detail, with the necessary band\ninformation included to enhance the clarity of the main ideas.\nResponse: Thanks for the constructive suggestions. To address the mentioned gaps, we have\nconducted comprehensive material characterizations (including TEM, EDS, XPS, AFM,\nRaman, etc.) and photoelectrical measurements (I-V characteristics, light pulse responses,\nelectrical pulse stimulation, and photoelectric coupling tests) to thoroughly validate its\nperformance. Following your suggestions, we have incorporated TEM, EDS, XPS and Raman\nanalysis in the revised manuscript, as shown in Fig. R7 (Fig. 1). The TEM and EDS results\nexplicitly demonstrate the well-defined interfacial structure between materials, while the XPS\ndata confirm the van der Waals stacking configuration between different material layers.\nRaman spectroscopy provides conclusive evidence for the successful construction of the\nheterojunction.\nFig. R7 (a) The TEM image and EDX mapping for the vertical cross-section of MoSe /h-BN/MoS\n2 2\nheterostructure. (b) The Raman spectra of MoS , h-BN and MoSe /h-BN/MoS heterostructure. (c)\n2 2 2\nResolved XPS spectra of Mo, Se, S, B and N core levels.\nThe corresponding contents and Fig. R8 have been updated in the revised manuscript.\nPage 4:\n“, we proposed volitional neuromorphic devices with extreme energy efficiency by\nsimulating the fovea response to regions of interest, configured with a van der Waals\nheterostructure of MoSe /h-BN/MoS (Fig. S1). The structural, interfacial,\n2 2\nmorphological and elementary characteristics are evaluated by conducting transmission\nelectron microscope (TEM), Raman, X-ray photoelectron spectroscopy (XPS), atomic\nforce microscope (AFM) and energy dispersive X-ray spectroscopy (EDS), respectively.\nAs shown in Fig. 1c, clear interfaces and hierarchical element distribution are observed\nin the vdWs heterostructure. The corresponding characteristic E (383 cm-1, 233 cm-1\n2g\nand 1350 cm-1) and A (408 cm-1) peaks are identified for few-layer MoS flakes (as\n1g 2\nfloating gate), MoSe flakes (as conduction channel) and h-BN flakes (as potential\n2\nbarrier), respectively (Fig. 1d)23–25. To further evaluate the interfacial characteristics,\nthe resolved XPS spectra of Mo 4f, Se 3d, S 2p, B 1s and N 1s core levels is analyzed in\nFig. 1e23–25, and the symmetric peak profiles of h-BN suggests that vdWs interactions\npredominantly govern the interfacial characteristics26. In addition, the morphology\nprofile and element distribution are recorded in Fig. S2 and S3, further confirming the\nsuccessful fabrication of proposed heterostucture with floating gate.”\nFig. R8 Schematic representation for (a) active volitional attention (AVA) modulation of and (b) the\nproposed volitional neuromorphic devices. Three key operations are included, incident light receiving\nfor photoelectric conversion, neural network training for spectral reconstruction, and a feedback\nmechanism for error calibration. (c) The TEM image and EDX mapping for the vertical cross-section\nof MoSe /h-BN/MoS heterostructure. (d) The Raman spectra of MoS , h-BN and MoSe /h-BN/MoS\n2 2 2 2 2\nheterostructure. (e) Resolved XPS of Mo, Se, S, B and N core levels.\nBesides, detailed photoresponse characteristics and floating-gate charge trapping mechanisms\nbased on the band structure information are analyzed, which significantly strengthens the\nphysical interpretation of device operation principles. The corresponding explanation has\nbeen updated:\nPage 17-18 in the Supplementary Information:\n“The band alignment plays a critical role in charge transport and trapping dynamics for\nproposed heterostructure devices. Specifically, MoS₂, MoSe and h-BN serve as floating\n2\ngate control layer, conductive channel layer and insulating barrier layer, respectively.\nUnder zero gate bias, MoSe (with a bandgap of ~1.4 eV) exhibits intrinsic n-type\n2\nbehavior due to selenium vacancies, while h-BN (a wide-bandgap semiconductor, ~5.76\neV) forms an electron-blocking barrier18,19. The valence band (VB) of h-BN aligns higher\nthan that of MoSe , creating a reverse-biased potential at h-BN/MoSe interface that\n2 2\nprevents vertical charge leakage. When the negative control gate voltage (-V ) is applied,\ng\nholes are accumulated in the MoS . After removing the control gate voltage, holes are\n2\ntrapped in the MoS layer and electrons are simultaneously accumulated in the MoSe\n2 2\nconduction channel. Upon illumination, photogenerated electron-hole pairs are\ngenerated in the MoSe , and the electrons in MoSe tend to tunnel through the h-BN\n2 2\nbarrier into the MoS₂ region. Meanwhile, holes remain confined in MoSe because the h-\ntunneling electrons with trapped holes in MoS , weakening the electric field control over\n2\nthe MoSe channel. This attenuation results in diminished electron accumulation under\n2\nreverse bias (negative photoconductance, NPC), while forward illumination enhances\nhole accumulation (positive photoconductance, PPC) through the same mechanism. The\ncritical dependence on band alignment, defect density in MoS , and h-BN barrier\n2\nthickness governs the charge storage kinetics and photoresponse characteristics of the\ndevice.”\nFig. R9 Positive and negative photoconductive mechanisms in 2D volitional neuromorphic devices.\nComment 2: Interface Information: There is no clear information regarding the interface\nformed between MoSe /h-BN/MoS₂. To clarify the interface's role and behavior, I\n2\nrecommend including XPS characterization data, which would provide insight into the van\nder Waals forces and interfacial interactions. The current characterization techniques, such as\nXRD and cross-sectional analysis, mainly reflect the crystalline behavior and do not provide\nsufficient details about the interface formation. This crucial aspect should be addressed with\nproper data and explanation.\nResponse: Thanks for the constructive suggestions. We have performed XPS to characterize\nthe interface behaviour of MoSe /h-BN/MoS heterostructure. The resolved XPS spectra of\n2 2\nMo 4f, Se 3d, S 2p, B 1s and N 1s core levels is recorded and analyzed. Particularly, the\nsymmetrical peak profile of h-BN as a barrier layer indicates that the vdWs interaction mainly\ncontrols the interface characteristics. Meanwhile, we have additionally conducted TEM and\nEDS (Fig. R7), and the interfaces can be clearly observed.\nTo emphasize the interface formation of proposed heterostructure, the corresponding contents\nand Fig. R7 have been updated in the revised manuscript. The modified content is consistent\nwith Comment 1.\nComment 3: Inconsistent Light Power Intensity: The manuscript mentions an incident light\npower of 0.1 nW in the main text, but Figure S5 presents data with a much higher intensity\nrange, from 0.1 mW to 5 mW. This discrepancy between the reported light power in the\nmanuscript and the data in the figures is concerning. The authors must clarify this\ninconsistency and ensure that the results align with the reported experimental conditions.\nResponse: Thank you for pointing this out. The incident power mentioned in the manuscript\nshould be 0.1 mW instead of 0.1 nW. We have carefully revised manuscript and made\ncorresponding corrections.\nPage 5:\n“, accompanied by exceptional responsivity, detectivity and noise-equivalent-power with\na low incident light power of 0.1 mW (Fig. S4–S6).”\nComment 4: Lack of Explanation on PPC and NPC Behavior: The manuscript claims that\nboth PPC and NPC exhibit good uniformity and reproducibility, with a rise time of less than\n160 μs (Fig. S8). However, there is no clear explanation of how V_GS biasing, light pulses,\nand V_DS are synchronized during the switching of these mechanisms. It is unclear how the\nphotoresponse varies with bias conditions. The authors should provide a detailed explanation\nof how PPC and NPC behave under different biasing conditions (V_GS, V_DS) and how this\ninfluences the photoresponse. Furthermore, the current experimental setup, as shown in\nFigure S9, does not provide valuable information regarding the three-terminal floating gate\nphototransistor. I suggest performing more detailed experiments under specific bias\nconditions to better characterize the behavior of the photoresponse.\nResponse: Thank you for your suggestion. The explanation of how V biasing, light pulses,\nGS\nand V are synchronized during the switching of these mechanisms has been added. The\nDS\nsynchronous switching of the three parameters is precisely controlled, especially the\nswitching between V pulses and optical pulses. In addition, we demonstrate that PPC and\nGS\nNPC are modulated by V (gate bias) and V (drain bias) through the following\nGS DS\nmechanisms:\n• Gate Bias (V ): Removal of negative V induces band bending in MoS , creating\nGS GS 2\nhole-trapping localized states. Concurrently, electron accumulation in MoSe forms a\n2\nhighly conductive channel.\n• Drain Bias (V ): Higher V strengthens the vertical electric field at the MoSe /h-\nDS DS 2\nBN/MoS interface. This enhances Fowler-Nordheim tunneling, reducing the\n2\ntunneling barrier and boosting photogenerated electron transfer from MoSe to MoS .\n2 2\nAccelerated electron-hole recombination neutralizes space charges in MoS ,\n2\nweakening its electrostatic screening on MoSe . Reduced carrier density in MoSe ’s\n2 2\nconduction band lowers channel conductivity under illumination, amplifying NPC.\nSimilar voltage-dependent tunneling and recombination dynamics govern PPC. Thus,\nlarger V directly amplifies photoresponse magnitude (PPC/NPC), while V\nDS GS\ncontrols the initial carrier distribution and trapping states27,28.\nThis interplay between bias-induced band engineering and tunneling efficiency explains the\ntunable photoresponse in such heterostructures.\nTo address the issues from reviewer, we have updated appropriate explanation and extra\nmeasurements in the revised manuscript and Supplementary Information.\nPage 6:\n“A single V pulse is initially applied for 1 s, and the light is simultaneously switched on\ng\nonce disconnecting the gate voltage.”\nPage 13-14 in the Supplementary Information:\n“Fig. S10 shows the modulation effect of the drain voltage on the PPC and NPC, which\ndemonstrates that larger drain voltages can cause larger response photocurrents. Upon\nthe removal of the negative gate bias, the MoS layer undergoes band bending-induced\n2\nhole trapping (forming localized states), while electron accumulation is established in\nthe MoSe conductive channel via carrier injection, rendering the channel in a highly\n2\nconductive state. Under elevated source-drain voltages, an enhanced vertical electric\nfield develops at the MoSe /h-BN/MoS₂ heterostructure interface. The intensified\n2\nelectric field strength effectively reduces the tunneling barrier height through Fowler-\nNordheim (F-N) tunneling mechanisms, thereby significantly enhancing the transfer\nefficiency of photogenerated electrons from the conduction band of MoSe to MoS 27,28.\n2 2\nThis accelerated electron-hole recombination process neutralizes the space charges\nwithin MoS , diminishing its electrostatic screening effect on the MoSe channel.\n2 2\nConsequently, the suppressed carrier density in the conduction band of MoSe manifests\n2\nas a pronounced reduction in channel conductivity, ultimately leading to the observed\nlower current state under illumination. This mechanism underpins the voltage-\ndependent enhancement of the NPC effect in such heterostructured systems. Similar to\nthe PPC effect.”\nRegarding to Fig. S9, we have further recorded the output curves of three-terminal floating\ngate phototransistors as a function of gate voltage (Fig. R10). Clearly, different gate voltages\ncan control devices in different conductance states.\nFig. R10 Output transfer curves as applying gate voltage from -40 to 40 V.\nThe corresponding context and Fig. R10 have been updated in the revised manuscript.\nPage 11 in the Supplementary Information:\n“Fig. S8(c) shows the output curve of the device under electrical pulses by sweeping the\nbias voltage from 0 to 2V. It is obvious that the different gate voltages can control\ndevices in different conductance states. The gate voltage modulates the device's\nconductivity by controlling charge trapping/release in the MoS floating gate. Negative\n2\nbias traps holes, enabling electron tunneling from MoSe through h-BN via light,\n2\nreducing channel electrons (high resistance). Positive bias preserves holes, sustaining\nelectron flow (low resistance).”\nComment 5: Issues with Figures and Plotting:\n1, Figure S11a: This figure is difficult to interpret, and the voltage sweeps and memory\nvoltage window are not distinguishable. Using different colors for each sweep voltage will\nimprove clarity and help differentiate the various conditions.\nResponse: Thank you for your suggestion. We have updated Fig. R11 with different colors\nfor each memory window.\nPage 16 in the Supplementary Information:\nFig. R11 Transfer characteristics at V of 1 V for different scanning gate voltages.\nds\n2, Figure S11c: The manuscript claims that a positive V_GS pulse results in a higher current\nresponse, while a negative V_GS pulse yields a much lower current. A detailed explanation\nis required to clarify whether this behavior is due to accumulation, depletion, or inversion.\nThe authors should provide a more thorough discussion of these mechanisms.\nResponse: Thank you for your suggestion. The gate voltage modulated current response can\nbe originated from the carrier accumulation/depletion dynamics and interface trapping effects,\nthe explanation is concluded as below:\n• Positive V pulse to electron accumulation: A positive V drives electrons back\nGS GS\nfrom MoS₂ to MoSe via tunneling, accumulating electrons in the MoSe channel.\n2 2\nThis enhances conductivity, resulting in a higher current state under drain bias.\n• Negative V pulse to hole trapping & depletion: Negative V induces hole trapping\nGS GS\nin MoS₂, creating localized states that deplete electrons in MoSe . Even after\n2\nremoving V , residual holes in MoS₂ maintain a depleted MoSe channel, locking the\nGS 2\nsystem in a lower current state.\nThe following discussion of above mechanisms has been updated.\nPage 16 in the Supplementary Information:\n“When V is swept towards positive, electrons are driven back from MoS to MoSe\ng 2 2\nthrough tunneling, and the current increases under the drive of the drain bias.\nMeanwhile, when the gate voltage is removed, due to the hole trapping effect of MoS , it\n2\nultimately remains in a lower current state than the initial current state. Subsequently,\nduring the erasing operation, the trapping effect at the MoS interface is relatively\n2\nstrong, causing the electrons in the MoSe channel to be depleted. After removing the\n2\nnegative gate voltage, the current level continues to increase compared to when the gate\nvoltage is applied under the mutual restriction of residual hole trapping centers and\ninterface tunneling effects.”\nComment 6: Non-Volatile Memory Behavior: The claim of non-volatile memory behavior is\nnot well substantiated in the manuscript. The memory retention data presented in Figure S9c\nand S9d is insufficient, and there is no clear correlation between the weight change of the\nmemory device and the applied light and voltage. The authors should address the following:\n1. Provide more detailed data on the memory retention at discrete levels.\nResponse: Thank you for your suggestion. We conducted a more detailed analysis of the\nimpact of sustained pulses on the device results, as shown in Fig. R12.\n Linear regime (<15 pulses):Unsaturated hole traps in MoS enable high linearity\n2\n(R²>0.97) with quantized current steps (~ΔI/step).\n Nonlinear regime (>15 pulses): Hole trap saturation reduces incremental charge per\npulse, diminishing step resolution. This validates trap-state density limits on multi-level\nmemory.\nFig. R12 Cumulative (a) positive and (b) negative photoconductivity measurements with progressive\nmultilevel states. The pulse width and interval of incident light are programmed as 200 ms and 1.5 s,\nrespectively. The linear variation of (c) PPC and (d) NPC with the number of pulses.\n2. Discuss how the photoresponse is combined with light and voltage to determine weight\nchange, and explain how the device's retention characteristics affect the weight levels.\nResponse: Thank you for your suggestion. In this work, the differential current obtained by\nNPC and PPC is mainly used to determine the change of weight. Therefore, for different gate\nvoltages, the response changes of PPC and NPC determine the weight changes. Spectral\nreconstruction accuracy is fundamentally governed by the resolution of differential\nphotocurrents. The differential photocurrent originates from device-level operations executed\nat nodes, adhering to Kirchhoff's law. Consequently, the weight modulation determined by\nthe combined optical and electrical stimuli corresponds to variations in differential currents,\nwhich are intrinsically linked to the interplay between PPC and NPC behaviors. The\ndifferential photocurrent employed in our framework critically determines the efficacy of\nmotion feature extraction. As demonstrated in Fig. R13, background noise amplifies with\nincreasing differential current, resulting in degraded reconstruction fidelity of object contours.\nSpecifically, elevated differential currents impair recognition accuracy and precision by\nerasing subtle features and merging object boundaries into noisy backgrounds. This\nunderscores the imperative of maintaining low differential currents to optimize edge detection\nand motion feature extraction.\nFig. R13 Image edge reconstruction effect corresponding to the different differential currents. (a)\nOriginal image. (b) Reconstructed image of I =2.68×10-10. (c) Reconstructed image of I\ndiff-pc diff-\n=1.19×10-8. (d) Reconstructed image of I =2.29×10-7.\npc diff-pc\nIn addition, for the AVA architecture, the maintenance level of memory effect does not\ndirectly determine the weight levels of system, but in high-speed motion scenes (<160 μs\nresponse), the carriers retained by Fowler Nordheim tunneling provide continuous inter frame\nsignal coherence for real-time differential computation (PPC/NPC) of neural networks,\navoiding target trajectory breakage caused by photoelectric response delay.\nThe active will regulation of AVA embeds the core logic of motion recognition into the\ndynamic weight update of neural networks, while the memory characteristics only serve as\nthe signal preprocessing module of physical hardware. Its non-ideality can be compensated by\nthe photoelectric current normalization at the algorithm end, achieving system level fault\ntolerance. The biomimetic advantage of AVA lies in transforming the attention mechanism\nfrom traditional \"memory dependent\" reconstruction (such as frame caching) to \"event\ndriven\" processing, where the influence weights are covered by the adaptive learning ability\nof neural networks.\n3. Perform experiments to demonstrate the weight linearity and retention at multiple levels,\nand clarify how many error-free weight levels can be achieved with high retention.\nResponse: Thank you for your suggestion. We have increased the number of pulses to 25 and\nobserved significant nonlinear changes, as evidenced in Fig.R14. This can be attributed to the\nsaturation of hole trap states in MoS , resulting in a decrease in the charge increment induced\n2\nby unit pulses and a nonlinear response.\n• Linear Regime (Pulse Count < 15):\nHigh-fidelity discrete levels: Photocurrent modulation shows near-perfect linearity\n(R² > 0.97), as each pulse fills unoccupied hole traps in MoS .\nenabling reliable multi-level memory encoding.\n• Nonlinear Regime (Pulse Count > 15):\nTrap-state saturation: Finite trap density (~15-pulse capacity) limits recombination\nsites, reducing incremental ΔI per pulse.\nDegraded discreteness: Post-saturation, photocurrent steps diminish nonlinearly,\ncompromising discrete-level resolution.\nThe corresponding explanation and Fig. R14 (Fig. S9) have been updated.\nFig. R14. The pulse width and interval of incident light are programmed as 200 ms and 1.5 s,\nrespectively. The linear variation of (c) PPC and (d) NPC with the number of pulses.\nPage 12 in the Supplementary Information:\n“The non-volatile photocurrent between the PPC and NPC was quantified across a\nrange of optical pulse counts by extracting the step photocurrent in Fig. S9(b, c). As\nevidenced by the linear fitting R-squared values exceeding 0.97 for both non-volatile\nphotocurrents when the pulse count is below 15, the excellent linearity between optical\npulse number and photocurrent is confirmed. However, when exceeding 15 pulses, the\nresponse gradually transitions to nonlinearity, which can be attributed to the finite\ndensity of trapped hole states in the MoS layer. During the initial phase (pulse count <\n2\n15), photoexcited electrons from each optical pulse effectively occupy unoccupied trap\nstates, enabling a linear correlation between photocurrent modulation and pulse\naccumulation. As the trap states approach saturation (pulse count > 15), the available\nrecombination sites diminish significantly. Subsequent electrons encounter reduced\nopportunities to recombine with trapped holes, thereby weakening the floating-gate\ncharge control efficiency per pulse.”\nComment 7: Weight Scheme and Device Performance: The manuscript lacks clear\ninformation on how the weight scheme is read. The authors should provide a detailed\nexplanation of how the device performance is evaluated in terms of weight change,\nparticularly in the context of light exposure and voltage application.\nResponse: Thank you for your suggestion. The device's weight is encoded in the differential\nphotocurrent, derived from the balance between PPC and NPC modes, modulated by light\nexposure and gate voltage. Light triggers carrier generation in the van der Waals\nheterojunction, while V controls charge trapping (NPC) or accumulation (PPC), dynamically\ng\ntuning I . By optimizing V for specific wavelengths, I is minimized, suppressing noise\ndiff g diff\nand sharpening object contours, and the differential photocurrent employed in our task\ndictates the effectiveness of motion feature extraction.\nFig. R13 illustrates that background noise intensifies with the increase of differential current,\nresulting in a less distinct reconstruction of object contours. Specifically, a higher differential\ncurrent diminishes the accuracy and precision of the recognition process by erasing small\nfeatures and blending the boundaries of objects into the noisy background. This underscores\nthe importance of small differential currents to enhance edge detection and motion feature\nextraction.\nTo provide a better readership, the operation of weight scheme has been added in revised\nmanuscript and Supplementary Information.\nPage 7:\n“A single V pulse is initially applied for 1 s, and the light is simultaneously switched on\ng\nonce disconnecting the gate voltage.”\nPage 19 in the Supplementary Information:\n“The impact of differential photocurrent on background noise and object recognition\nclarity is clarified. As shown in Fig. S13(a-c), the background noise becomes more\npronounced when the differential current increases, leading to a less distinct\nreconstruction of the object's contours. Noteworthily, larger differential currents result\nin the loss of fine details, blurring object boundaries and noisy background, thereby\ndiminishing the accuracy and resolution of the recognition process.”\nComment 8: Response to Voltage Sweep (Figure S11a): The authors should explore how the\nmemory window would change when considering a voltage sweep from -40V to 40V.\nAdditionally, the manuscript should explain how the memory window is affected by light\nexposure (red, blue, and green) in both the presence and absence of light.\nResponse: Thank you for your suggestion. We have measured the memory window (ΔV) and\nextracted V under different scanning windows from -40V to 40V. Figure R15(b)\ng-max\ndemonstrates the linear relationship between ΔV and V , suggesting an adjustable storage\ng-max\nwindow. This can be attributed to the tunable storage window of MoS originating from\n2\nelectric field-dependent charge trapping and defect-mediated carrier dynamics. As the gate\nvoltage scanning window increases, a stronger interfacial electric field drives more charges\ninto spatially distributed defects or interface traps through Fowler-Nordheim tunneling29. The\natomic thin structure weakens carrier shielding, achieving linear ΔV-V scaling.\ng-max\nIn addition, we have performed corresponding measurements of memory windows to confirm\nthe impacts from gate voltage and illumination, as shown in Fig. R15 (Fig. S11). Under red,\ngreen, and blue light illumination, the memory window of the floating-gate transistor remains\nstable, but the stored charge increases significantly. This is explained by photon-assisted\ntunneling and band modulation. Photons at different wavelengths do not directly affect charge\ntunneling or storage barrier height, maintaining memory stability. However, they excite\ncarriers in the channel or interfacial regions, enabling additional charge injection into the\nfloating gate and increasing stored charge density. Furthermore, photo-induced interface\nstates or defects may provide extra charge trapping sites. Despite this, the\nmemory window remains unchanged, indicating that optical fields optimize charge storage\ncapacity without altering the device's inherent storage barrier structure.\nFig. R15 (a) Transfer characteristics at V of 1 V for different scanning gate voltages. (b) The\nds\nrelationship between ΔV and V . (c) Transfer characteristics at V of 1 V under red, green,\ng-max ds\nand blue light illumination. (d) The retention performance of memory after write and erase\noperations.\nTo address reviewer’s concern, following explanation has been updated:\nPage 14-16 in the Supplementary Information:\n“This can be attributed to the tunable storage window of MoS originating from electric\n2\nfield-dependent charge trapping and defect-mediated carrier dynamics. As the gate\nvoltage scanning window increases, a stronger interfacial electric field drives more\ncharges into spatially distributed defects or interface traps through Fowler-Nordheim\ntunneling. The atomic thin structure weakens carrier shielding, achieving linear ΔV-V\ng-\nscaling.”\nmax\n“As shown in Fig. S11(c), under red, green, and blue light illumination, the memory\nwindow of the floating-gate transistor exhibits no significant variation, while the stored\ncharge quantity demonstrates a marked increase. Under optical excitation, photon\nenergies may fall below the bandgap of either the tunneling layer or floating-gate\nmaterial, thereby preventing direct photon-induced charge tunneling or modification of\nstorage barrier height, which maintains memory window stability. However,\nphotogenerated carriers can be excited in the channel or interfacial regions, enhancing\ncharge injection efficiency under electric field driving forces. This facilitates increased\nelectron capture into the floating gate through the tunneling layer, leading to elevated\nstored charge density. This observation indicates that optical fields can indirectly\noptimize charge storage capacity by modulating carrier concentration without altering\nthe inherent storage barrier structure of the device.”\nComment 9: Calculation of Optimal Gate Voltage in Figures 2D and 2E: There is no clear\nexplanation provided for how the optimal gate voltage in Figure 2D (\"Photocurrent mapping\nas a function of gate voltage within the spectrum ranging from 450 to 800 nm, with the gate\nvoltage ranging from -40 to 40 V in steps of 2 V and a bias voltage of 1 V\") and Figure 2E\n(\"The histogram of the optimal gate voltage corresponding to the minimum differential\nphotoresponse, with each color representing its corresponding wavelength\") is calculated.\nThese explanations are essential to clarify how these values are determined and how they\nare used for further simulations. The authors should provide a clear and detailed\nmethodology for how the optimal gate voltages (V_G^(diff-min)) are calculated and how\nthese values influence the subsequent simulations and results.\nResponse: Thank you for your suggestion and we are sorry for the confusion. Actually, the\noptimal gate voltage (Fig. 2e) is originated from selection of the minimum differential\nphotocurrent (the difference of NPC and PPC, measured as a function of gate voltage within\nthe spectral range of 450-800 nm, Fig. 2d). To evaluate the optimal gate voltage combination,\nwe iterate over all positive and negative gate voltage combinations and use Kirchhoff's law to\ncalculate the corresponding differential current. By sorting the differential currents, the\nminimum differential photocurrent and the corresponding optimal set of gate voltages are\nobtained. In fact, there are some very close values of the minimum differential photocurrent,\nwe present only one of the optimal grid voltages (the voltages corresponding to the upper and\nlower edges of the color block are a set of optimal gate voltages).\nNotably, the measured NPC and PPC is the necessary basis for the implementation of\ndifferential operation on the device. The variation of the spectral response of the device\noriginated from gate voltage is obvious, therefore it is beneficial for the subsequent spectral\nreconstruction.\nThe spatial-domain differential operations (edge enhancement and extraction) and temporal-\ndomain differential operations (motion feature extraction) that we performed using the PPC\nand NPC functions of the device are shown in Fig. R16 and R17 (Fig. S14 and Fig, S15 in the\nSupplementary Information), respectively. The differential photocurrent actually originates\nfrom the device-level operations performed at the nodes following Kirchhoff's law by the\nnon-volatile positively and negatively photoconductive photocurrent generated by two\ndevices operating at positive and negative gate voltages.\nFig. R16 The retinomorphic vision sensor based on a vdW vertical heterostructure for simultaneous\nimage sensing and processing: (a) Edge extraction principle: The image is grayscale encoded, and after\ninterval on/off processing, differential operation is performed. These operations are realized by\ncontrolling the photoresponse of each pixel in the sensor by varying V independently (b)\ng\nDemonstration of image processing with three different operations: 1, adjacent columns; 2, adjacent\nrows; 3, interval inverted; Photo credits: Logo of Optoelectrical Detection and Sensor group.\nFig. R17 The mechanism of motion recognition based differential operation for positive (P ) and\nmxn\nnegative (N ) conductivity in volitional neuromorphic devices, and a demonstration of the original\nmxn\nimage and the recognized image.\nOur purpose of choosing the most suitable voltage is to get the clearest effect of motion\nfeature extraction, which extracts high-frequency information, i.e., information that changes\ndrastically, from the global information. Therefore, the differential method of calculating the\nchange values is widely used to extract high-frequency information. On this basis, the\ndifferential photocurrent used in our task determines the effect of motion feature extraction.\nFig. R13 (Fig. S13 in the Supplementary Information) shows that the background noise\nintensifies with increasing differential current, resulting in a less clear reconstruction of the\nobject's outlines. This emphasises how crucial it is to reduce differential current in order to\nenhance edge detection and motion feature extraction.\nIn order to better help readers understand this process, we have also made corresponding\nmodifications in the revised manuscript.\nPage 7：\n“As mentioned earlier, differential current (I ) obeying the Kirchhoff's law is essential\ndiff\nfor realizing visual attention, and the smaller I , the better the object contour clarity,\ndiff\nas evidenced in Fig. S13.”\n“In addition, spatial-temporal operations (edge enhancement and extraction) of static\nand dynamic object are validated by manipulating PPC/NPC characteristics of the\ndevice (Figs. S14-S17).”\nPage 9:\n“Taking the spectral information at 733 nm as an example, the V mapping for this\ng\nwavelength can be derived (Fig 3e). By sorting the differential currents, the minimum\ndifferential photocurrent and the corresponding optimal set of gate voltages are\nobtained. Multiple minima of I values are identified, guiding the subsequent\ndiff-pc\nspectral reconstruction process for each voltage combination.”\nAdditional information\nResponse: We have further polished the Abstract. In addition, all the changes made in the\nrevised manuscript leave no impact on our conclusion.\n“The technological revolution driven by artificial intelligence (AI) has significantly\nimproved the hardware performance, but energy consumption remains a critical\nbottleneck. The state-of-the-art retinomorphic devices, as core components of AI\nhardware, excel in feature extraction but are constrained by passive attention\nmechanisms that lack flexibility of actively extracting additional features. Inspired by\nthe human visual system, this work introduces a volitional neuromorphic device with\nactive volitional attention (AVA) regulation. By leveraging gate-voltage-tunable\nphotoconductance to generate adjustable differential spectral response and employing\nneural networks to evaluate spectral reconstruction accuracy, the device achieves\nselective task perception. Experimental results demonstrate a data compression ratio of\n1.17% and an extreme information energy efficiency of 0.625 pJ/bit. This advancement\nnot only advances retinomorphic hardware design but also provides a sustainable\npathway for energy-efficient hyperspectral imaging and next-generation neuromorphic\ncomputing systems.”\nReference\n1. Theeuwes, J. Top-down and bottom-up control of visual selection. Acta Psychol.\n(Amst). 135, 77–99 (2010).\n2. Pang, J. et al. Reconfigurable perovskite X-ray detector for intelligent imaging. Nat.\nCommun. 15, 1–9 (2024).\n3. Zhang, Z. et al. All-in-one two-dimensional retinomorphic hardware device for motion\ndetection and recognition. Nat. Nanotechnol. 17, 27–32 (2022).\n4. Sun, H. et al. Multi-functional synaptic memristor for neuromorphic pattern\nrecognition and image compression. Mater. Today Phys. 52, 101684 (2025).\n5. Ng, S. E. et al. Retinomorphic Color Perception Based on Opponent Process Enabled\nby Perovskite Bipolar Photodetectors. Adv. Mater. 2406568, 1–9 (2024).\n6. Dang, Z. et al. Object Motion Detection Enabled by Reconfigurable Neuromorphic\nVision Sensor under Ferroelectric Modulation. ACS Nano (2024)\ndoi:10.1021/acsnano.4c10231.\n7. Jiménez-Bravo, D. M., Lozano Murciego, Á., Sales Mendes, A., Sánchez San Blás, H.\n& Bajo, J. 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Mater.\n31, 1–10 (2021).\n14. Li, Y. et al. Reconfigurable, Nonvolatile, Optoelectronic Synaptic Memtransistor\nBased on MoS2/Te van der Waals Heterostructures. Adv. Funct. Mater. 2423333, 1–\n10 (2025).\n15. Liu, Z. et al. All-In-One Optoelectronic Transistors for Bio-Inspired Visual System.\nAdv. Mater. 2409520, 1–11 (2024).\n16. Li, N. et al. Gate-tunable large-scale flexible monolayer MoS2 devices for\nphotodetectors and optoelectronic synapses. Nano Res. 15, 5418–5424 (2022).\n17. Li, G. et al. Photo-induced non-volatile VO2 phase transition for neuromorphic\nultraviolet sensors. Nat. Commun. 13, 1–9 (2022).\n18. Cassabois, G., Valvin, P. & Gil, B. semiconductor. 10, (2016).\n19. Huang, Y. et al. High-Performance Broadband Visible−Near Infrared Photodetector\nEnabled by Atomic Capping Layer. Adv. Opt. Mater. 10, 1–10 (2022).\n20. Function, S. Mathematics of Control , Signals , and Systems Approximation by\nSuperpositions of a Sigmoidal Function * yrx a ( t )__, fl unit. 303–314 (1989).\n21. Tolstikhin, I. et al. MLP-Mixer: An all-MLP Architecture for Vision. Adv. Neural Inf.\nProcess. Syst. 29, 24261–24272 (2021).\n22. Liu, R., Li, Y., Tao, L., Liang, D. & Zheng, H. T. Are we ready for a new paradigm\nshift? A survey on visual deep MLP. Patterns 3, (2022).\n23. Chen, X. et al. Fabry-Perot interference and piezo-phototronic effect enhanced flexible\nMoS2 photodetector. Nano Res. 15, 4395–4402 (2022).\n24. Pan, S. et al. Light trapping enhanced broadband photodetection and imaging based on\nMoSe2/pyramid Si vdW heterojunction. Nano Res. 16, 10552–10558 (2023).\n25. Fukamachi, S. et al. Large-area synthesis and transfer of multilayer hexagonal boron\nnitride for enhanced graphene device arrays. Nat. Electron. 6, 126–136 (2023).\n26. Lin, Y. C. et al. Low Energy Implantation into Transition-Metal Dichalcogenide\nMonolayers to Form Janus Structures. ACS Nano 14, 3896–3896 (2020).\n27. Wang, Y. et al. Negative Photoconductance in van der Waals Heterostructure-Based\nFloating Gate Phototransistor. ACS Nano 12, 9513–9520 (2018).\n28. Zhang, Z. et al. All-in-one two-dimensional retinomorphic hardware device for motion\ndetection and recognition. Nat. Nanotechnol. 17, 27–32 (2022).\n29. Migliato Marega, G. et al. A large-scale integrated vector–matrix multiplication\nprocessor based on monolayer molybdenum disulfide memories. Nat. Electron. 6,\n991–998 (2023)."}, {"round": 3, "reviewer_comments": {"reviewer_3": "Review Response\nThe authors have provided some additional experiments and simulations; however, major concerns raised in my previous review remain partially or insufficiently addressed. Please note the following critical points that still require clear elaboration:\nMissing Fabricated Array Image and SEM:\nDespite claims of fabricating multiple devices, the manuscript still lacks a clear SEM image of the full fabricated device array. An optical/SEM image of the entire array layout is essential to validate how devices are integrated, their spacing, and interconnection scheme. A schematic diagram showing the entire array structure (dimensions, pixel arrangement, wiring, etc.) must also be included for clarity.\nDevice-to-Device Variation and Repeatability:\nWhile some Raman and electrical data from six devices have been provided, the core concern remains unresolved:\nThe flask transfer method is inherently prone to large variability. The manuscript must clearly describe the procedures, controls, and metrics used to ensure uniformity and performance stability across multiple devices. What steps were taken to minimize layer thickness variation, contact misalignment, or charge-trapping inconsistency? How is device-to-device variation quantified, and what metrics define acceptable reproducibility?\nArray Size and Functionality:\nIt remains unclear how many devices are part of the final array and how these devices are operated in parallel or sequence. The authors need to specify the array size (e.g., 3×3, 10×10) and show its operation in a system-level experiment (e.g., image sensing or pattern recognition task across multiple elements).\nCode-Figure Mapping:\nWhile the authors provided GitHub links and some implementation details, the provided code should be clearly linked to corresponding figure numbers in the manuscript. Please specify which figure uses which part of the code or dataset. This mapping is crucial to improve reproducibility and allow reviewers and readers to trace each computational result back to its source."}, "author_response": "Response to Reviewer #1\nReviewer #1 (Remarks to the Author):\nThe authors have provided a greatly revised manuscript. My concerns have been well\naddressed. Now I recommend it for publishing.\nResponse: We deeply appreciate the positive comments and recognition for our work.\nResponse to Reviewer #2\nReviewer #2 (Remarks to the Author):\nAll my comments have been well addressed in the revised manuscript, i have no further\nquestion and happy to recommend it for publication.\nResponse: We deeply appreciate the positive comments and recognition for our work.\nResponse to Reviewer #3\nReviewer #3 (Remarks to the Author):\nAuthor failed to answer first part of my question 1 which is clear limitation of this work:\n(1) The manuscript appears more like an array simulation in real-time, yet the actual\ncase involves the fabrication of a single phototransistor with a floating gate. The large-\nscale production, repeatability, and performance of such a device will pose significant\nchallenges, as highlighted in the manuscript's claim: \"As a proof of concept, we use the\nMoSe /h-BN/MoS single device to receive each pixel image one by one to perform\n2 2\noptical images and preprocess them for edge detection by programming their\nphotocurrent valve for the voltage.\" However, there is no information on device-to-\ndevice verification, repeatability, or performance across multiple devices.\nResponse: Thank you for the comments. To fully address your concerns on device-to-\ndevice verification, repeatability or performance, we have fabricated six more\nneuromorphic devices under the same fabrication condition, and performed the\ncorresponding material (i.e., Raman spectra) and device (i.e., photoconductive\nresponses and charge storage) characterizations, as well as device simulations (i.e.,\naccuracy change curve and recognition accuracy).\n• Material Verification\nFig. R1 shows the Raman spectra of MoSe /h-BN/MoS heterostructure for six\n2 2\nvolitional neuromorphic devices. Clearly, all devices demonstrate similar characteristic\npeaks, suggesting good structural integrity of each constituent material and\nreproducibility of each device, as evidenced by the absence of significant shifts in\ncorresponding characteristic vibrational modes: the E mode at 383 cm-1 and A mode\n2g 1g\nat 408 cm-1 for MoS ; the A mode at 233 cm-1 for MoSe , and the E mode at 1350\n2 1g 2 2g\ncm-1 for h-BN.\nFig. R1 The Raman spectra of MoSe /h-BN/MoS heterostructure for six different\n2 2\nneuromorphic devices.\n• Device Verification\nWe have validated the sensing, memory and computing functionalities for each device.\nAs shown in Fig. R2, all six devices consistently demonstrated the essential positive\nand negative photoconductive photocurrent (PPC and NPC). The dark current and\nphotocurrent of all devices remained stable and consistent. In addition, the PPC and\nNPC behaviors are repeatable and switchable at gate voltages of -20 V and 20 V.\nFig. R2 Positive and negative photoconductive photoresponse of the six different\nneuromorphic devices at gate voltages of -20 V and 20 V.\nFurthermore, the retention performance of six as-fabricated neuromorphic devices has\nbeen examined, as shown in Fig. R3. Despite slight variations are observed in the stored\ncharge density, all devices still maintain a robust and constant memory window of\napproximately 78 V. This confirms that the fundamental operational principles,\nincluding the complementary conductivity used for image pixel allocation, are reliably\nrepeatable across different devices.\nFig. R3 Transfer characteristics at V of 1 V under -50-50 V scanning gate voltages of\nds\nthe six different neuromorphic devices.\n• Simulation Verification\nTo further demonstrate the practical repeatability of our devices, we have conducted\nsimulations of an attention recognition task using the data from these different devices.\nTaking the 697 nm light illumination (red light) as an example, the state characterized\nby the maximum variation in peak reconstruction accuracy is consistently observed for\neach device at this specific wavelength position, as shown in Fig. R4. In addition, a\nhigh recognition accuracy exceeding 93% for various spectral objects (i.e., red, green\nand blue) is achieved (Fig. R5). Notably, the recognition accuracy is considerably\nstable, which is varying between 93%~97% among six devices. These results further\nconfirm the reproducibility of device performance and repeatability of practical\nrecognition accuracy.\nFig. R4 The accuracy change curve for the red colors of the six different neuromorphic\ndevices.\nFig. R5 The recognition accuracy of different spectral features object-based AVA of the\nsix different neuromorphic devices.\nTo clarify the device-to-device verification and repeatability, the corresponding\nstatement and Fig. R1-R5 (Fig. S25-29) have been updated in the Revised Manuscript\nand Supplementary Information:\nPage 5 in the Revised Manuscript:\n“Notably, good device-to-device repeatability has been verified through additional\nvalidations of the vdWs heterostructure, device performance, and accuracy\nsimulations. (Fig. S25-29).”\nPage 38-41 in the Supplementary Information:\n“To address the critical concern regarding device-to-device variation, we\nconducted a repeatability validation experiment. Firstly, six volitional\nneuromorphic devices were fabricated under identical conditions. The structural\nintegrity of constituent materials (MoS2: E2g at 383 cm-1, A1g at 408 cm-1);\nMoSe2: A1g at 233 cm-1; h-BN: E2g at 1350 cm-1) was confirmed by Raman\nspectroscopy (Fig. S25), as evidenced by the absence of significant shifts in these\ncharacteristic vibrational modes. Subsequently, the function of integrated sensing-\n sensing-\nmemory-computing is verified. As shown in Fig. S26 and 27, all devices\nconsistently exhibited both PPC and NPC responses. The photocurrent remained\nstable under applied gate voltages of -20 V and 20 V. Despite slight fluctuations in\nthe stored charge density, all devices maintain memory windows of approximately\nwhose operational principles align with the previous PPC/NPC switching\nframework. Apart from the consistency of device performance, the repeatability\nin the simulation of attention recognition has also been validated. Taking the 697\nnm (red light) wavelength as an example, the state characterized by the maximum\nvariation in peak reconstruction accuracy is consistently observed for every device\nat this specific wavelength position (Fig. S28). Moreover, for all six devices, the\naverage recognition accuracy has exceeded 93% for objects with different spectral\nfeatures, as shown in Fig. S29.”\n(2) Author should supplement the code for review and upload the code on github for\nfair publication process and provide code in supplementary zip file\nAlso developed code complete execution protocol of simulator should be provided step\nbt step\nAlso provide developed simulator and upload on github and supplement complete zip\nfile as supplementary file\nResponse: We greatly appreciate your suggestions. Yes, the code, execution protocol\nand simulator are essential for a fair and transparent publication process. We have\nuploaded the code on GitHub, and provided the code and a step-by-step execution\nprotocol of the simulator in the Appendix. The developed simulator and all relevant\ncode have been uploaded on GitHub and summarized as part of the supplementary\nmaterials.\n• Manuscript code:\nhttps://github.com/IFFS-ODS/SpectrumReconstruction_ResFCN\n• Manuscript dataset:\nhttps://huggingface.co/datasets/IFFS-ODS/SpectrumReconstruction_Diff\nThe corresponding verification of this study have been pronounced in the revised\nmanuscript and Supplementary Information.:\nAppendix: Implementation and Utilization of the ResFCNet Architecture\n1.1 All software dependencies and operating systems\nThe project has been tested on Windows 10 and Ubuntu 18.04 LTS.\n1.2 Versions the software has been tested on\nThe project has been tested with the following configuration: CUDA 11.2, Python 3.8.x,\nTensorFlow 2.9.0 (GPU version), Keras 2.9.0, NumPy 1.24.3, Matplotlib 3.7.2, Scikit-\nlearn 1.3.0 and Pandas 2.0.3.\nThe implementation of the Residual Fully Connected Network (ResFCNet) architecture\ndescribed in this study is made available through a public repository at\nhttps://github.com/IFFS-ODS/SpectrumReconstruction_ResFCN. The architecture is\nrealized in TensorFlow 2.x and Keras, employing both identity and dense residual\nblocks to facilitate the training of deep neural networks for regression tasks.\nThe neural network architecture consists of an input layer accepting 16-dimensional\nfeature vectors, followed by a sequence of residual blocks with progressively increasing\ndimensionality (256, 512, and 1024 units respectively). Each residual block is\ncomposed of multiple dense layers with regularization and ReLU activation functions,\nwith skip connections to preserve gradient flow in deep network configurations. The\noutput layer is designed to produce 1650-dimensional vectors representing the\nreconstructed spectrum, facilitated through a series of linear transformations (1024,\n2048, and finally 1650 units).\nThe dataset required for model training and evaluation is hosted on the Hugging Face\nplatform at https://huggingface.co/datasets/IFFS-ODS/SpectrumReconstruction_Diff.\nThe dataset comprises two essential files: feature_16.txt containing the 16-dimensional\ninput features, and label_1650.txt containing the corresponding 1650-dimensional\nspectral outputs. These files can be acquired directly through HTTP requests,\neliminating the necessity for specialized data-loading libraries. For reproducibility\npurposes, the dataset is randomly partitioned using a train-test split of 80:20, with an\nadditional 10% of the training data reserved for validation during the training process.\nNo additional preprocessing or normalization is applied to the data in the current\nimplementation, though researchers adapting this approach to alternative datasets may\nneed to incorporate appropriate normalization techniques depending on the specific\ncharacteristics of their data.\nTo reproduce the results presented in this study, the following execution protocol is\nrecommended:\nThe code should be executed in an environment with TensorFlow 2.x and the requisite\ndependencies installed. GPU acceleration is strongly advised due to the computational\ndemands of training deep neural networks. The training procedure will automatically\nemploy early stopping with a patience parameter of 30 epochs, monitoring validation\nloss to determine the optimal point at which to terminate training and prevent\noverfitting. The model with the lowest validation loss is preserved and subsequently\nutilized for evaluation on the test dataset.\nUpon completion of the training procedure, the implementation automatically generates\nvisualization artifacts including loss curves illustrating the convergence behavior\nduring training, and comparative plots of predicted versus actual spectral outputs for a\nsubset of test samples with the lowest validation losses. Additionally, quantitative\nperformance metrics including mean squared error (MSE) and coefficient of\ndetermination (R²) are computed and reported for the test dataset, providing objective\nmeasures of the model's predictive accuracy.\nResearchers seeking to adapt this implementation to alternative regression tasks should\nprimarily focus on modifying the input and output dimensions in the model architecture\nto accommodate their specific data characteristics. The modular design of the residual\nblocks facilitates architectural experimentation, allowing for the adjustment of network\ndepth and width through the addition or removal of residual blocks and modification of\nthe unit parameters. Additionally, hyperparameters including the regularization\ncoefficient, learning rate schedule, and batch size may be tuned to optimize\nperformance for specific datasets and computational resources.\nFor deployment in resource-constrained environments, model quantization and pruning\ntechniques may be applied to reduce the computational and memory requirements,\nthough such optimizations fall outside the scope of the current implementation and\nwould require additional post-training processing.\nThe computational requirements for training the ResFCNet architecture are non-trivial,\nparticularly for larger datasets. The implementation is optimized for GPU acceleration\nand includes memory growth management to maximize GPU utilization while\npreventing memory overflow errors. Training duration is measured and reported,\nproviding an empirical basis for estimating computational requirements when applying\nthis approach to new datasets. Researchers with limited computational resources may\nconsider reducing the network depth or width, or decreasing the batch size.\nAdditional Information\nConsidering the sober writing style, we have carefully revised the manuscript by\nrefraining from listed words. All the changes made in the revised manuscript leave no\nimpact on our conclusions.\nResponse to Reviewer #3\nReviewer #3 (Remarks to the Author):\nThe authors have provided some additional experiments and simulations; however,\nmajor concerns raised in my previous review remain partially or insufficiently\naddressed. Please note the following critical points that still require clear elaboration:\nResponse: We sincerely appreciate insightful feedback and constructive suggestions,\nwhich will significantly help in improving the clarity and impact of our manuscript.\nMissing Fabricated Array Image and SEM:\nDespite claims of fabricating multiple devices, the manuscript still lacks a clear\nSEM image of the full fabricated device array. An optical/SEM image of the entire\narray layout is essential to validate how devices are integrated, their spacing, and\ninterconnection scheme. A schematic diagram showing the entire array structure\n(dimensions, pixel arrangement, wiring, etc.) must also be included for clarity.\nResponse: We appreciate your concern of the missing device array. The optical and\nSEM images of the full fabricated device array (3×3) are shown in Figure R1 and R2\n(Fig. S30 and S31, Supplementary Information), providing a detailed view of the\narray layout and confirming the integration and spacing of the devices. The\ncorresponding fabrication process for the 3×3 array, consisting of MoS /h-BN/MoSe\n2 2\nheterostructure, is detailed as follows: Large sheets of MoS and h-BN were\n2\ntransferred to the substrate using a dry transfer method. The materials were then\npatterned into a 3×3 array using electron-beam lithography and reactive-ion etching.\nTo obtain large, clean flakes of MoSe , we first attached them to a substrate using a\n2\ndry transfer method before using a wet transfer process to place them onto the h-BN\ntransistor array. Finally, using EBL and RIE again, we designed and etched the\nelectrodes for the nine blocks, separating the entire van der Waals structure into nine\ndistinct, all-in-one devices. We believe that the newly included images, along with the\ndetailed description of the fabrication process, will sufficiently address your concerns\nregarding the device array's structure and integration.\nFig. R1 Optical images of the volitional neuromorphic device array. (a) Optical image\nof full 3×3 array. (b) The enlarged view of an individual device.\nFig. R2 SEM images of the volitional neuromorphic device array. (a) Full SEM image\nof 3×3 array with source and drain electrodes. (b) The enlarged view of the nine\ndevices.\nTo clarify the layout of the fabricated device array, the corresponding statement and\nFig. R1-R2 (Fig. S30 and S31) have been updated in the Supplementary Information:\nPage 41-42 in the Supplementary Information:\n“3×3 array based volitional neuromorphic devices\nLarge sheets of MoS and h-BN were transferred to the substrate using a dry\n2\ntransfer method. The materials were then patterned into a 3×3 array using\nelectron-beam lithography (EBL) and reactive-ion etching (RIE). To obtain large\nand clean flakes of MoSe , we first attached them to a substrate using a dry\n2\ntransfer method before using a wet transfer process to place them onto the h-BN\narray. Simultaneously, to avoid direct connection between MoSe and the MoS ,\n2 2\nthe size of the etched MoSe is smaller than that of h-BN and MoS by using EBL\n2 2\nand RIE again in Fig. S30. Finally, we designed and etched the electrodes for the\nnine blocks, separating the entire van der Waals structure into nine distinct\nneuromorphic devices, as shown in Fig. S30 and S31. Despite slight device-to-\ndevice variations (Fig. S32), the essential functional switching mechanism remain\nconsistently robust.”\nDevice-to-Device Variation and Repeatability:\nWhile some Raman and electrical data from six devices have been provided, the core\nconcern remains unresolved:\nThe flask transfer method is inherently prone to large variability. The manuscript must\nclearly describe the procedures, controls, and metrics used to ensure uniformity and\nperformance stability across multiple devices. What steps were taken to minimize\nlayer thickness variation, contact misalignment, or charge-trapping inconsistency?\nHow is device-to-device variation quantified, and what metrics define acceptable\nreproducibility?\nResponse: We used standardized fabrication protocols (as explained in Experimental\nSection, refer to Md Gius Uddin et.al1 and Zhenhan Zhang et.al2) to fabricate devices,\nand ensured baseline consistency by prescreening materials for each device (via AFM\nand Raman), followed by measuring PPC and NPC characteristics across multiple\nindividually fabricated devices to access the device-to-device variations. The key\nsteps of minimizing layer thickness variation, contact misalignment, or charge-\ntrapping inconsistency can be concluded as pre-transfer characterization and selection\nof materials, all-dry viscoelastic stamping, and post-transfer high-vacuum annealing\ncombined with high-precision lithography.\nFor reproducibility, we have measured PPC and NPC (the essential functional\nbehavior) of fabricated 3×3 device array, all devices exhibit reliable switching\nbetween distinct PPC and NPC states but with slight variance, as shown in Fig R3. We\ndefine this functional consistency as our metric for acceptable reproducibility, as it is\nthe foundational requirement for the active attention mechanism we proposed.\nAlthough we have successfully fabricated a 3×3 device array, reproducibility and\nfabrication variability remain challenging, which would be further investigated in our\nnext work.\nFig. R3 Positive and negative photoconductive photoresponse of the nine different\nneuromorphic devices-based 3×3 array at gate voltages of -20 V and 20 V.\nTo validate the device-to-device variation and repeatability, the following content and\nFig. R3 (Fig. S32) have been updated in the Supplementary Information:\nPage 38 in Supplementary Information\nwe conducted rigorous multi-device validation experiments using an all-dry\nviscoelastic stamping flow. Layer thickness was first controlled by selecting\nmechanically exfoliated flakes pre-characterized by Raman spectroscopy and\nAFM to verify layer count and surface quality. These selected flakes were then\ntransferred using a micromanipulator-controlled viscoelastic stamp (Materials:\nPDMS), an all-dry method that prevents contamination and physical damage.\nAfter transfer, a high-vacuum anneal (e.g., 300 °C, <10-6 Torr) was performed to\nremove adsorbates and ensure a clean material-substrate interface, which\nminimizes charge trapping and improves electrical stability. Finally, geometric\nuniformity was achieved by using high-resolution electron-beam lithography\n(EBL) to define contacts, aligning them to pre-patterned markers with a\ntolerance of less than 0.5 μm.\nPage 42 in the Supplementary Information\n“Despite slight device-to-device variations (Fig. S32), the essential functional\nswitching mechanism remain consistently robust.”\nArray Size and Functionality:\nIt remains unclear how many devices are part of the final array and how these devices\nare operated in parallel or sequence. The authors need to specify the array size (e.g.,\n3×3, 10×10) and show its operation in a system-level experiment (e.g., image sensing\nor pattern recognition task across multiple elements).\nResponse: We have fabricated and characterized the volitional neuromorphic device\narray with array size of 3×3, which are operated in parallel. The corresponding array\nconfiguration is explicitly stated in the experimental section, and the corresponding\ncharacterization data are substantiated the device's functional feasibility.\nThe primary scope of this study deliberately focuses on establishing foundational\nprinciples at the device level. Our objective was to validate the fundamental\nfeasibility and underlying physics of this novel device architecture for implementing\nAVA mechanisms. Therefore, the main contribution of work is the device-level proof\nthat our component can efficiently execute the functionalities of attention-based\nmodels.\nWe indeed concur that a system-level demonstration, such as image sensing or pattern\nrecognition, is important for future AI technology. However, our study focuses on\nvalidating a novel concept with a single device or a small-scale array, aligning with\nestablished practice in pioneering neuromorphic and in-memory computing research.\nFor instance, Zhang et al. used a 2D retinomorphic device to achieve separation\ndetection of moving objects2, Huang et al. developed programmable optoelectronic\nsensors for in-sensor data compression3, and Ng et al. created perovskite\nphotodetectors that emulate the retina's opponent process4. These seminal works\ndemonstrate a common principle: the scientific merit of a new device concept is first\nestablished in a single or few-element configuration. Such a proof-of-concept is a\ncrucial prerequisite for subsequent, engineering-focused efforts toward large-scale\nintegration. Accordingly, our work is positioned as a foundational study intended to\nmotivate and guide future system-level explorations.\nTo clarify the future research focus and direction of this work, we have provided\nexplanations in the Revised Manuscript.\nPage 16 in the Revised Manuscript:\n“Furthermore, we have successfully fabricated a 3×3 array device (Fig. S30 and\nS31). Despite slight device-to-device variations (Fig. S32), the essential functional\nswitching mechanism remain consistently robust. Future work will therefore\nfocus on refining heterostructure fabrication process to enhance device-to-device\nreproducibility and fabrication variability which represents one of the critical\nsteps for scaling towards intelligent systems.”\nCode-Figure Mapping:\nWhile the authors provided GitHub links and some implementation details, the\nprovided code should be clearly linked to corresponding figure numbers in the\nmanuscript. Please specify which figure uses which part of the code or dataset. This\nmapping is crucial to improve reproducibility and allow reviewers and readers to trace\neach computational result back to its source.\nResponse: Thank you for your valuable feedback. We have revised our manuscript to\nmore clearly link our code and data to the figures, thereby enhancing reproducibility.\nWe have already uploaded our primary dataset to Hugging Face and open-sourced the\ncore code and implementation details on GitHub. To address the reviewer's concern\ndirectly, here is a detailed mapping: The neural network architecture, as described in\nthe code, strictly corresponds to Figures 3(a-b) and Fig. S18.\nThe training process is handled by running ResFCNet.py. This script produces the\nvalidation and training convergence curves of the model loss, which are presented in\nFig. S19. It also generates 12 comparison images of reconstructed spectra, showcasing\nthe model's performance.\nTo reproduce the figures related to spectral reconstruction, such as Figures 3(c-f) and\nFig. S20, one can use the trained model parameters. By feeding the data from\nfeature_16.txt into the trained model, you can obtain the predicted values. These\npredicted values are then compared with the standard output from label_1650.txt, and\nthe resulting figures are generated using plotting softwares like Origin.\nTo further facilitate reproducibility for reviewers and readers, we have open-sourced a\nnew script, predict.py, on GitHub. This script loads the trained model and dataset,\nperforms predictions on all feature data, and saves the results to a predictions.txt file.\nFor accuracy calculation, predict.py computes and outputs the average and overall\nR2-accuracy values. It also saves the R2-accuracy score for each individual sample to\na r2_scores.txt file.\nWe believe these additions will make it significantly easier for reviewers and readers\nto trace our computational results back to their source, ensuring full transparency and\nreproducibility. For any other analyses or results of interest, the provided code and\ndataset can be easily adapted and processed to generate new insights.\nTo clearly illustrate the code-figure mapping, the corresponding statement has been\nupdated in the Revised Manuscript and Supplementary Information:\nPage 17 in the Revised Manuscript:\n“To ensure full transparency and reproducibility of our work, the primary\ndataset has been deposited in the Hugging Face repository\n(https://huggingface.co/datasets/IFFS-ODS/SpectrumReconstruction_Diff), and\nthe core implementation code is publicly available on GitHub\n(https://github.com/IFFS-ODS/SpectrumReconstruction_ResFCN). For a\ncomprehensive guide on how to reproduce the specific results and figures\npresented in this work, including the model training (Fig. S19) and spectral\nreconstructions (Fig. 3(c-f) and Fig. S20), please refer to the Reproducibility\nGuide in the Supplementary Information.”\nPage 46 in the Supplementary Information：\n“7 Code-Figure Mapping\nTo facilitate the reproduction of our computational results, this section provides\na clear mapping between our code, datasets, and the figures presented in the\nmain manuscript.\nA. Neural Network Architecture\nFig. 3(a-b) and S18: These figures illustrate the neural network architecture. The\ncorresponding implementation is defined in the core model scripts available in\nour GitHub repository.\nB. Model Training and Validation\nFig. S19: This figure displays the training and validation loss convergence\ncurves. These results can be generated by running the ResFCNet.py script, which\nhandles the complete model training process. The script also generates 12\ncomparison images of reconstructed spectra to showcase the model's\nperformance during training.\nC. Spectral Reconstruction and Analysis\nFig. 3(c-f) and S20: These figures show the reconstructed spectra compared to\nthe standard ground truth. To reproduce these results:\nUse our newly provided predict.py script, which loads the pre-trained model\nparameters and the dataset.The script feeds the input data (e.g., from\nfeature_16.txt) into the trained model to get predicted spectral values.The\npredicted values can then be plotted against the standard output data (from\nlabel_1650.txt) using standard plotting software like Origin to generate the final\nfigures.\nD. Quantitative Accuracy and Prediction Generation To further enhance\nreproducibility, the predict.py script performs the following key functions:\nPrediction Generation: It runs predictions on all feature data and saves the\ncomplete set of results into a predictions.txt file.\nAccuracy Calculation: It computes the R²-accuracy for each individual sample\nand saves these scores to a r2_scores.txt file. Additionally, it calculates and\noutputs the average and overall R²-accuracy values for a comprehensive\nperformance evaluation.”\nReferences\n1. Uddin, M. G. et al. Broadband miniaturized spectrometers with a van der\nWaals tunnel diode. Nat. Commun. 15, 571 (2024).\n2. Zhang, Z. et al. All-in-one two-dimensional retinomorphic hardware device for\nmotion detection and recognition. Nat. Nanotechnol. 17, 27–32 (2022).\n3. Huang, H. et al. In-sensor compressing via programmable optoelectronic\nsensors based on van der Waals heterostructures for intelligent machine vision.\nNat. Commun. 16, 3836 (2025).\n4. Ng, S. E. et al. Retinomorphic Color Perception Based on Opponent Process\nEnabled by Perovskite Bipolar Photodetectors. Adv. Mater. 36, 1–9 (2024)."}, {"round": 4, "reviewer_comments": {"reviewer_3": "Authors has addressed all my concern"}, "author_response": ""}]

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