Ultra-wide spectrum photosynapse array with 64k-scale for neuromorphic fusion imaging

Domain: physics
Published: 2026-01-06
Editorial outcome: major_revision

Abstract
Bio-inspired photosynapse hardware with wide spectrum information fusion imaging and neuromorphic processing is promising for complex environment perception. However, integrating ultra-wide spectrum responsivity within a single photosynapse device and scaling to a large array has remained challenging. Here, we demonstrate a 64k-scale (65,536 pixels) ultraviolet-to-mid-infrared photosynapse array based on carbon nanotube/molybdenum oxide heterojunctions. Typical photosynapse behaviors are confirmed in the wide spectrum region of 365 nm (ultraviolet), 532 nm (visible), 1064 nm (near infrared), and 10.6 μm (mid-infrared). A polydopamine-mediated surface treatment enables homogeneous deposition of carbon nanotube/molybdenum oxide films across heterogeneous transistor substrates with 99.96% yield. The photosynapse array enhances image frame similarity from 0.897 to 0.965 in dynamic trajectory prediction. Furthermore, the wide spectrum neuromorphic fusion imaging using photosynapse array achieves 99.58% accuracy in trajectory recognition under challenging conditions, surpassing the 63.93% with visible light alone. This work contributes to paving the way for next-generation autonomous perception.

Introduction
Next-generation autonomous systems require robust perception capabilities in diverse and challenging environments such as haze, fog, and nighttime1,2. These systems must reliably recognize objects, interpret scenes, and predict trajectories to ensure safe navigation and real-time decision-making3,4. Current automotive sensing relies predominantly on visible (Vis) spectrum complementary metal oxide semiconductor (CMOS) sensors. For example, dynamic vision sensors (DVS) have demonstrates impressive performance in challenging conditions5,6. However, these sensors remain limited to the visible spectrum, whereas many critical autonomous navigation scenarios require perception beyond visible wavelengths7–11. In this regard, wide spectrum fusion imaging beyond Vis information offers a promising solution12–14. For example, the fusion of infrared (IR) with Vis imaging provides complementary sensing capabilities when Vis spectrum performance degrades, as IR wavelengths experience reduced Mie scattering in particulate matter such as fog and smoke compared to Vis light, enabling more effective transmission in diverse and challenging environments14.

However, implementing wide spectrum sensing faces significant architectural and device challenges15. The separation between sensing and processing units requires complex preprocessing pipelines for each spectrum band16. A typical pipeline includes sequential stages of sensing, signal conditioning, analog-to-digital conversion (ADC), and data storage/retrieval17. For wide spectrum systems, existing methods primarily require multiple discrete sensors for different spectrum12, necessitating numerous sensor-to-processor pathways that collectively increase physical footprint, power consumption, and system complexity. Additionally, such multi-sensor configurations require sophisticated image algorithms to reconcile the positional misalignment and resolution inconsistencies across different sensor types18. Therefore, there is an urgent need for advanced device and architecture that can simultaneously achieve wide spectrum fusion imaging with efficient in situ processing capabilities.

Fortunately, neuromorphic photosynapse hardware could provide a promising solution for above challenges19. Their synapse-mimicked behaviors enable in situ optical information processing at the pixel level during the sensing process without requiring external circuitry. Recent advances20–35 have already demonstrated their efficacy in ultraviolet (UV) or Vis spectrum, including successful implementations of UV image preprocessing24 and Vis light reservoir computing27. These capabilities have been validated through software-level simulations21,26,27,31,34 or small-scale (typically <32×32) array implementations23–25,28–30,32,33 (Supplementary Table 1), establishing a solid foundation for expanding into the integration of ultra-wide spectrum fusion imaging and energy-efficient neuromorphic processing.

Despite these advances, implementing a large-scale photosynapse array with ultra wide spectrum response, especially extending beyond the current UV-to-Vis range to infrared range, remains challenging. First, it is difficult to realize the synapse temporal dynamics behavior for low-energy photons such as near-infrared (NIR) and mid-infrared (MIR) because the high background of thermally-excited carriers in narrowband gap semiconductors leads to rapid carrier recombination. Second, scaling a single device to a large-scale array is hindered by surface energy mismatches between heterogeneous-surface transistor readout circuitry and active materials, resulting in compositional heterogeneity and compromised device-to-device performance consistency.

Here, we demonstrate a 64k (65536 pixels)-scale UV-to-MIR photosynapse array based on carbon nanotube/molybdenum oxide (CNT/MoO3) heterojunctions. The photocarriers in narrow bandgap CNT undergo band alignment-assisted vacancy carrier trapping in MoO3, facilitating the synapse-mimicking dynamics (Fig. 1a). Typical photosynapse response behaviors are confirmed in the wide spectrum region of 365 nm (UV), 532 nm (Vis), 1064 nm (NIR), and 10.6 μm (MIR), respectively. A polydopamine-mediated treatment renders heterogeneous transistor substrate surfaces homogeneous, facilitating CNT/MoO3 film deposition and contributing the high-yield photosynapse array. The demonstrated array exhibits the wide spectrum response range and the high integration scale compared to previously reported photosynapse results (Fig. 1b, c, Supplementary Table 1). The neuromorphic information processing capacity in photosynapse hardware improves dynamic trajectory prediction tasks with image similarity from 0.897 to 0.965 compared to conventional photosensor device. Additionally, the fusion imaging of Vis and MIR achieves an accuracy of 99.58% in trajectory recognition under challenging environmental conditions, outperforming the 63.93% accuracy achieved when using Vis light alone. This integration of wide spectrum sensing and neuromorphic processing in a large-scale array represents a significant advance in hardware-based visual information processing, offering promising possibilities for robust perception systems in diverse and challenging operational environments.

Results
Design of ultra-wide spectrum photosynapse device
To integrate wide spectrum sensing with neuromorphic processing, we design a photosynapse device based on CNT/MoO3 heterojunctions. As illustrated in Fig. 1a, the device features a vertical paradigm with an indium tin oxide (ITO) electrode and a CNT/MoO3 active layer, where CNT conductive pathways with dispersed MoO3 nanoparticles creating heterojunctions interfaces. This design utilizes complementary material properties: the narrow bandgap of CNT enables effective absorption from UV to MIR region, while the wide bandgap MoO3 with abundant oxygen vacancy defect states provides ultraviolet response and carrier trapping dynamics.

The preparation of CNT/MoO3 film employed water-based processes (see Methods for details), which has potential for large-scale integration in subsequent sections. High-resolution transmission electron microscopy (HRTEM) reveals well-defined crystalline domains of MoO3 with lattice spacing of 0.230 nm (Supplementary Fig. 1)36, which is in agreement with X-ray diffraction patterns (Supplementary Fig. 2a). Raman spectroscopy provides further confirmation of the hybrid material composition. The CNT/MoO3 composite exhibited characteristic Raman peaks at 820 cm⁻¹ and 995 cm⁻¹, attributed to the stretching modes of Mo-O-Mo and Mo=O bonds in MoO3, respectively37 (Supplementary Fig. 2b). The characteristic G-band (1590 cm⁻¹) and D-band (1350 cm⁻¹) signatures of CNT were also clearly resolved37. These results demonstrate that CNT/MoO3 films were successfully prepared using water-based method.

Fabrication of large-scale photosynapse array
Large-scale photosynapse array fabrication faces primary challenges originating from substrate heterogeneity, which significantly impacts device uniformity across arrays (Fig. 2a). For example, standard CMOS or thin film transistor (TFT) platforms typically feature heterogeneous surfaces comprising passivation layers (e.g., SiOx, SiNx) and patterned electrodes38,39 (e.g., Cu metal, ITO), which create material interfaces with distinct surface properties. To investigate this issue, we fabricated TFT array structures following standard semiconductor processing protocols, with ITO electrode arrays and SiNx passivation layers as the critical top interface materials. Detailed fabrication processes are provided in the Supplementary Fig. 3 and Supplementary Note 1. As illustrated in Fig. 2b, the original substrates exhibit significant water contact angle disparities between ITO (~42.9±2.1°) and SiNx (~84.5±2.5°) region (mean±standard deviation). These interfacial contact angle disparities compromise film integrity during CNT/MoO3 deposition, resulting in uneven material distribution across material interfaces and crack formation on surfaces with higher contact angles. Figure 2c reveals characteristic cracking patterns on unmodified SiNx surfaces, highlighting the demand for surface treatment strategy.

We propose polydopamine (PDA) modification as an effective solution to this problem. PDA is a catecholamine-derived polymer with strong adhesive properties that forms robust interfacial bonds with various substrate surfaces38,39. The PDA modification procedure is detailed in the Methods section. In contrast to original substrate, PDA-treated surfaces demonstrate consistent contact angles on ITO (27.0±1.6°) and SiNx (25.0±3.0°), with substantially reduced contact angles compared to their initial states, facilitating uniform material deposition. The materials deposited after polydopamine treatment exhibit crack-free characteristics (Fig. 2c), maintaining uniformity even at the ITO-SiNx interface regions (Supplementary Fig. 4).

The underlying mechanism for this uniformity enhancement was characterized. X-ray photoelectron spectroscopy (XPS) analysis confirms effective binding between PDA and the heterogeneous substrate surfaces. On SiNx substrates, the Si 2p peak exhibits characteristic broadening and a positive shift in binding energy after PDA modification (Supplementary Fig. 5a), suggesting the effective interaction between SiNx and PDA40. Similarly, for ITO surfaces, the Sn 3d5/2 peak shows an increased binding energy from 486.5 eV to 486.7 eV after PDA modification (Supplementary Fig. 5b), confirming interfacial interaction between ITO and PDA41. The uniform contact angle achieved across heterogeneous ITO/SiNx substrates originates from this interfacial adhesion capability and PDA intrinsic surface properties38,39. The deposited PDA layer presents abundant surface functional groups such as quinone and secondary amines, which dictate its uniform wetting behavior24. The contact angle remained stable after 30 days of ambient storage (Supplementary Fig. 6), illustrating the good stability of PDA modification. Optical characterization shows PDA absorption spanning UV to MIR wavelengths. However, in the UV-Vis-NIR regions, photogenerated carriers suffer from poor transport in the amorphous polymer42,43, while MIR absorption induces molecular vibrations without carrier generation, indicating limited contribution to the photoelectric response. XPS analysis of Mo 3d spectra shows nearly identical Mo5+/Mo6+ ratios (1:2.03 vs 1:2.05) with and without PDA modification. These results indicate that PDA functions primarily as a surface modifier without significantly altering the photoelectronic properties of the active layers (see detailed analysis in Supplementary Note 2 and Supplementary Figs. 7, 8).

To evaluate the reliability and scalability of our PDA modification approach, we characterized the electrical uniformity of medium-scale (4k-scale, 4096 pixels) and large-scale (64k-scale, 65536 pixels) photosynapse arrays based on dark current relative non-uniformity (DCRNU), photo response non-uniformity (PRNU) and Moran’s I index analysis. DCRNU reflects the pixel-to-pixel variation in dark current normalized to the mean dark signal, which is defined as:
DCRNU = (σ_dark / μ_dark) * 100% (1)
where σ_dark is the standard deviation of dark current across all pixels, and μ_dark is the mean dark current. PRNU reflects the pixel-to-pixel variation in photo response normalized to the effective signal, which is defined as:
PRNU = (σ_photo / μ_photo - μ_dark) * 100% (2)
where σ_photo is the standard deviation of photocurrent, μ_photo is the mean photocurrent, and μ_dark is the mean dark current. Moran’s I index reflects the spatial autocorrelation of pixel current across the array, which is defined as:
I_Moran = (N / W) * (Σ_i,j w_ij (I_pixel,i - I_pixel_mean) * (I_pixel,j - I_pixel_mean)) / (Σ_i (I_pixel,i - I_pixel_mean)^2) (3)
where N is the total number of pixels, I_pixel is the pixel current value, I_pixel_mean is the mean current. We used a queen contiguity configuration where pixels sharing edges or corners are considered adjacent. The spatial weight w_ij was defined using a queen contiguity configuration where pixels sharing edges or corners were assigned a value of 1 and 0 otherwise, with W representing the sum of all weights.

As illustrated in Supplementary Fig. 9, the 4k array under dark conditions demonstrates a mean current of 34.6±1.2 nA with a DCRNU of 3.4%. Spatial correlation analysis yielded a Moran’s I index of 0.3010 for dark current, indicating moderate spatial clustering and good electrical uniformity. The calculated PRNU yielded 13.2% for the 4k array. All pixels in the 4k array were functional, resulting in 100% yield. Figure 2d, f showed the dark current and current spatial distribution and statistical result of the 64k array, respectively. The 64k array showed a mean dark current of 7.9±0.8 nA with the DCRNU of 10.1%. The Moran’s I indices of 0.3010 (64×64 array) and 0.7613 (256×256 array) indicate that the dark current is distributed randomly across the array, with no significant spatial clustering (see Supplementary Note 3 for detailed analysis). These edge effects caused by topographical variations where interconnects route to peripheral pads, become more pronounced in larger arrays, as supported by the spatial clustering of failed pixels in edge regions (Supplementary Fig. 10). Future potential improvements include optimized peripheral routing topology to minimize edge effect, combined with adaptive non-uniformity correction (NUC) algorithms employing two-point calibration or scene-based statistical methods to compensate for residual fixed-pattern noise44,45.

Figure 2e, f exhibited the light current spatial distribution and statistical result, respectively. The calculated PRNU was 27.7% under illumination at 532 nm, which is comparable to the variability observed in conventional CMOS image sensors46. The corresponding PRNU values at 365 nm, 1064 nm, and 10.6 μm were 27.3%, 29.4%, and 30.2%, respectively (see Supplementary Table 2 and Supplementary Note 4 for details). These results confirm the PDA modification approach for reliable large-scale photosynapse array integration (Fig. 2g).

Characterization of ultra-wide spectrum photosynapse
The as-proposed CNT/MoO3 device demonstrates photosynapse behaviors across an ultra-wide spectrum from UV to MIR wavelengths. As shown in Fig. 3a-b, under a series of ten consecutive optical pulse stimulation of 10.6 μm MIR (0.25 Hz, ON/OFF=2s/2s), the post-synapse current exhibits progressive enhancement with subsequent pulses. A saturation effect is observed in the post-synapse current, where the current increment gradually diminishes with subsequent pulses until reaching a plateau. This saturation behavior is analogous to the depletion of readily releasable neurotransmitter vesicle pools in biological synapses, where continued stimulation leads to diminishing postsynapse responses until vesicle recycling occurs47,48. Following cessation of the optical pulse train, the photoresponse exhibits a gradual decay profile, analogous to the retention characteristics of biological synapses49,50. When the stimulation frequency increases to 0.5 Hz (ON/OFF=1s/1s, Supplementary Fig. 11), the short inter-pulse intervals prevent complete current decay between pulses, resulting in accumulation of successive current responses. This photoresponse mimics low-pass filtering properties characteristic of biological synaptic dynamics, where the exponential decay creates a temporal integration window that preferentially transmits low-frequency signals while attenuating high-frequency components49. Such behavior differs from conventional photoconductive devices, which typically exhibit static photoresponse without history-dependent modulation.

Figure 3c–f presents the post-synapse current behavior of the device under dual-pulse stimulation with varying time intervals across different wavelengths, all exhibiting significant paired-pulse facilitation effects.

To evaluate the linearity and dynamic range of our photosynapse devices, we characterized the intensity-dependent photoresponse across the wide spectrum range (Supplementary Figs. 15, 16). Detailed wavelength-dependent responsivity measurements demonstrate effective wide-spectrum photoresponse from UV to MIR range (Supplementary Fig. 15). The photocurrent exhibits a sublinear power-law dependence on incident optical power (I_ph ∝ P^α) with α < 1 across all tested wavelengths from UV to MIR, suggesting trap-related photoresponse processes51 (see Supplementary Note 6 for detailed analysis). Device-to-device uniformity measurements demonstrate consistent photosynapse behavior (Fig. 3l and Supplementary Fig. 17).

These results suggest robust photosynapse functionality across an ultra-wide spectrum range covering UV to MIR. To the best of our knowledge, this represents the widest spectrum coverage exceeding previously reported ranges for photosynapse devices (Fig. 3g, Supplementary Table 1). This wide spectrum photoresponse spanning visible to 10.6 μm wavelengths suggests that our photosynapse device could potentially simplify multiple imaging sensors such as RGB and IR camera in autonomous vehicles into a single device, reducing system complexity while maintaining multi-spectrum perception capabilities52,53. Besides, the device exhibits low power consumption of 23.9 nW per pixel at 0.5 V bias (Supplementary Note 8), demonstrating potential for energy-efficient perception systems.

To understand this exceptional performance, we characterized the electronic and optical properties of photosynapse device. XPS analysis demonstrates the coexistence of Mo6+ and Mo5+ oxidation states27 (Fig. 3h) and three distinct oxygen environments32 (Fig. 3i), confirming oxygen vacancy traps within the MoO354,55. Optical measurements reveal complementary absorption properties for ultra-wide spectrum response. UV-Vis spectroscopy and tauc plot analysis show 3.3 eV indirect bandgap56 of MoO3 with characteristic oxygen vacancy trap-assisted absorption36 at ~600 nm (Fig. 2j). Fourier transform infrared spectroscopy (FTIR) and tauc plot fitting results confirm a narrow direct bandgap of 0.094 eV in CNT (Fig. 2k), which is consistent with previous literature57.

Since MIR radiation can induce thermal effects through absorption in device components including active materials, substrate and ITO electrodes (Supplementary Fig. 19), we conducted systematic experiments to investigate the contribution of thermally-induced conductivity changes for synapse behaviors. Thermal imaging of the entire device under operating conditions reveals a temperature rise of 2.8 K during MIR illumination, contributing ~1.2 nA to the total photocurrent. (Supplementary Fig. 20). Pulse-train experiments support that synaptic plasticity originates from photocarrier trapping rather than thermal accumulation (Supplementary Fig. 21), with stable ohmic contacts maintained within the temperature range. Detailed analysis of these thermal effects is provided in Supplementary Note 7. Based on these results, we can elucidate the mechanism underlying the observed photosynapse behavior. Upon illumination, incident photons are absorbed primarily by CNT across the ultra-wide spectrum (UV to MIR) due to the narrow bandgap, while MoO3 contributes significantly to absorption in the UV region. The work function difference between MoO3 (6.16 eV) and CNT (3.92 eV) creates an asymmetric interfacial energy landscape (see more detailed band alignment measurement in Supplementary Note 5) that drives photoexcited electrons from CNT to MoO3 upon illumination58,59. These electrons are subsequently captured by oxygen vacancy traps in MoO3. This process of band alignment-driven carrier transfer and oxygen vacancies mediated carrier trapping creates an electrostatic field that effectively modulates carrier concentration in adjacent CNT channels, producing the observed photosynapse dynamics. The wavelength-dependent decay differences can be attributed to the influence of carrier generation mechanisms and trap states filling levels. Higher-energy UV and Vis photons generate carriers in both CNTs and MoO3, leading to efficient trap filling and faster trap-assisted recombination with relatively short decay time constants60,61. In contrast, MIR photons (10.6 μm) generate relatively low-density carriers only through narrow-bandgap CNT absorption, resulting in partial trap filling and slower detrapping as well as recombination processes with extended decay times62. Additionally, MIR illumination induces a local temperature rise (Supplementary Fig. 19), and the thermal relaxation after illumination cessation may further contribute to the extended decay63. This photosynapse device with moderate time constants is suitable for applications without stringent speed requirements while benefiting from memory effects.

Demonstration of static and dynamic imaging
The ultra-wide spectrum photosynapse array with large-scale enables direct hardware-level imaging. To demonstrate this capability, we investigated the array performance in differentiating materials with varied spectrum absorption characteristics. When imaging complex multi-material objects, wide spectrum approaches reveal compositional details that remain indistinguishable under single-wavelength illumination18. As illustrated in Fig. 4a, certain materials exhibit strong absorption under ultraviolet light, while in the visible range, these same materials show distinctive contrast patterns that enable detailed discrimination.

We conducted experimental validation using two distinct glass container types: transparent borosilicate glass and transition metal oxide-doped UV-opaque glass. Both containers incorporated metallic ring inclusions. The experimental configurations, including embedded metallic ring and encapsulating glass, are documented in Fig. 4b, e. Comparative imaging analysis under 365 nm UV and 532 nm Vis illumination reveals distinct characteristics. Group I exhibited similar object visibility across both spectral regions (Fig. 4c, f). In contrast, Group II displayed marked wavelength-dependent behavior: 365 nm UV imaging reveals only exterior morphology due to strong surface absorption, while 532 nm Vis imaging (Fig. 4d, g) enabled clear visualization of internal metallic components. These results demonstrate that wide spectrum imaging provides more comprehensive object information, enabling detection of concealed components that remain insufficiently contrasted under limited-spectrum illumination. Furthermore, raster-scanning imaging in the MIR (1064 nm) and MIR (10.6 μm) regions yielded well-resolved patterns (Supplementary Fig. 22), confirming the array’s imaging functionality across the ultra-wide spectrum range. Passive thermal imaging of room-temperature objects without external illumination, however, remains challenging. This limitation may be attributed to the relatively large noise from residual metallic CNTs and 1/f noise in amorphous silicon TFTs. Potential strategies include achieving higher semiconducting CNT purity through advanced sorting techniques64, and integrating silicon CMOS readout circuits65 for improved noise performance.

Beyond static imaging, the photosynapse array offers advantages in dynamic scene analysis through its inherent spatial-temporal response characteristics. Exponential decay-based temporal encoding is well-established in neuromorphic vision systems66. Our photosynapse implements this principle across the UV-to-MIR spectrum in hardware through stimulus-dependent conductance retention and gradual photocurrent decay. This intrinsic integration function creates a system where current amplitude directly indicates stimulus recency, with recently activated pixels maintaining higher states while earlier activated regions exhibit progressive attenuation. Consequently, each frame inherently encodes a time-weighted history of motion trajectories. The wide-spectrum photosynapse array have the potential for trajectory tracking both visible and non-visible dynamic features. Moreover, our photosynapse architecture offers the potential to avoid temporal surface reconstruction, as the accumulated conductance state can be directly readout, simplifying prediction tasks that would otherwise require reconstructing temporal surfaces from event streams.

We quantified this advantage using a cycle-consistent generative adversarial network (CycleGAN) framework67,68 that exploits embedded temporal correlations. As shown in Fig. 5b. The architecture employs bidirectional generator-discriminator pairs that map between sequential states (t→t+1, t+1→t), maintaining trajectory physics constraints through cycle consistency mechanisms. To isolate temporal integration effects, we established two comparative datasets: photosynapse trajectories captured using precision-controlled laser stimulation (Fig. 5c) and simulated conventional sensor responses created through Hadamard product operations. The latter preserved only current position information while eliminating historical context, effectively replicating traditional sensors without persistent states.

We employed the Structural Similarity Index (SSIM) to quantitatively evaluate prediction fidelity. The higher SSIM values indicating better preservation of structural information. Photosynapse-based prediction achieved a significantly higher SSIM value of 0.965±0.018 between predicted and ground truth frames, compared to 0.897±0.032 for conventional approaches (Fig. 5d), representing an improvement in prediction accuracy. Frame analysis (Fig. 5e–j) demonstrates trajectory prediction capabilities comparing photosynapse and conventional sensor approaches. The photosynapse input (Fig. 5e) exhibits intrinsic temporal integration through visible trajectory trails, while conventional sensor input (Fig. 5h) captures only current position information. When compared to real next frames (Fig. 5f, i), predictions based on photosynapse image maintain trajectory coherence and spatial accuracy (Fig. 5g), whereas conventional sensor predictions (Fig. 5j) show reduced fidelity with motion path deviation.

Demonstration of neuromorphic fusion imaging
Following our investigation of dynamic trajectory prediction capabilities, we examined the critical challenge of maintaining perception reliability in adverse environmental conditions. We developed an experimental platform specifically designed to evaluate wide spectrum fusion imaging under simulated atmospheric attenuation. The setup utilized wavelength-dependent laser beam trajectories (10.6 μm MIR and 532 nm Vis) as representative signals for vehicle motion, with an optical filter serving as an analog for atmospheric interference effects such as fog, haze, or smoke (Fig. 6a). These conditions typically attenuate visible light transmission while permitting greater penetration of infrared wavelengths.

The wide spectrum capability of our photosynapse array allows for enhanced perception through fusion imaging, where information from multiple wavelengths (λ1, λ2, …, λk) contributes to a comprehensive representation. The fusion process combines these inputs according to:
I(i,j,fusion) = Σ w_k * I(i,j,λ_k) (4)
where I(i,j,fusion) represents the fused pixel value at position (i,j), w_k represent the relative contribution weight of each wavelength, and I(i,j,λ_k) is the pixel value at position (i,j) for wavelength λ_k.

The experimental protocol incorporated straight and turn trajectory patterns as classification targets, evaluating different imaging configurations: photosynapse array with visible light only (Fig. 6b), photosynapse array with wide spectrum fusion (MIR+Vis) (Fig. 6c) (See Method for details). To ensure fair comparison between photosynapse neuromorphic imaging and conventional hardware imaging capabilities, we constructed parallel datasets by applying a Hadamard product between photosynapse imaging data and binary masks of light spot positions (Supplementary Fig. 23, Supplementary Note 2), effectively preserving spatial information while neutralizing the temporal dynamics advantage.

Performance analysis reveals significant advantages of wide spectrum fusion imaging in challenging environments. Representative wide spectrum neuromorphic fusion images of last frame were presented in Fig. 6d. Learning curves demonstrates that photosynapse hardware utilizing both Vis and MIR wavelengths achieved substantially higher classification accuracy compared to Vis-only sensing under identical environmental conditions (Fig. 6e). Notably, even when both systems utilized the same wavelength fusion, the photosynapse array outperformed conventional hardware by ~37% (Supplementary Fig. 24). Two-fold cross-validation with six parallel tests confirm these findings, with confusion matrices revealing classification accuracies of 99.58% for photosynapse fusion imaging compared to only 63.93% for Vis-only imaging (Fig. 6f, g). To assess classification stability, we evaluated the recognition performance across 10 independent training runs with different random weight initializations (Supplementary Fig. 25). The consistent high accuracy (99.75% ± 0.75%) suggests that images captured by our photosynapse array support stable classification performance. Further analysis of different MIR:Vis fusion ratios showed that classification performance improved with increasing MIR contribution and stabilized at high accuracy level, confirming the robustness of the fusion approach (Supplementary Figs. 26, 27). To further evaluate the contribution of visible-light fusion under various atmospheric conditions, we analyzed our system’ performance using the M3FD dataset with visibility-dependent attenuation based on established atmospheric transmission models69,70. The results demonstrate that wide-spectrum fusion consistently enhances classification accuracy compared to MIR-only imaging across different visibility conditions, suggesting the contribution of visible-light features even under atmospheric attenuation.

We further evaluated performance comparison of our photosynapse array with DVS cameras66,70–72 (see Supplementary Note 11 for details). Under good visibility (100 km), both systems achieve comparable performance (Supplementary Fig. 29). However, under low visibility conditions (1 km), DVS-based methods experience significant performance degradation (accuracy drops to 66.7%~79.2%), as atmospheric attenuation reduces the contrast in visible wavelengths that DVS sensors rely on for event generation73,74. In contrast, our photosynapse array with fused Visible and MIR maintains 97.9% accuracy by utilizing the better penetration of MIR wavelengths through atmospheric obscurants. This demonstrates that the wide-spectrum photosynapse array can complement visible DVS systems by maintaining robust classification in challenging conditions, suggesting potential for combined systems that utilize both technologies’ strengths in future applications.

Methods
Preparation of CNT/MoO3 film
MoO3 nanoparticles were synthesized via a low-temperature oxidation method. A hydrogen peroxide solution (10 mL H2O2 and 20 mL deionized water) was added dropwise to molybdenum powder (0.0417 mol) under continuous magnetic stirring to ensure effective reaction. The reaction was maintained at room temperature under static conditions for 96 h to facilitate complete reaction. The as-synthesized MoO3 dispersion was then mixed with multi-walled carbon nanotube aqueous solution (1wt%) in a volume ratio of 1:1. The mixed MoO3/CNT precursor solution was used for subsequent deposition.

Process of PDA modification on heterogenous substrate
The polydopamine (PDA) coatings on heterogenous substrate through a CuSO4/H2O2-catalyzed oxidation process. The reaction solution was formulated by dissolving dopamine hydrochloride at a concentration of 2 mg mL−1 in a 50 mM Tris buffer solution (pH 8.5) containing 5 mM CuSO4 and 19.6 mM H2O2. The substrates were immersed in the precursor solution for 5 min for PDA deposition. Following immersion, the samples were thoroughly cleaned with deionized water and dried under vacuum at 30 °C for 4 h to remove residual moisture.

Material and device characterization
X-ray photoelectron spectroscopy (XPS) measurement was conducted on Shimadzu Corporation (AXIS Supra). Raman spectrum was using HORIBA Corporation (HR800) under 532 nm laser excitation. The 10.6 μm light source utilized a Synrad Model 48-1 CO2 laser; the 1064 nm light source employed a Coherent Compass 1064–4000 Nd:YAG laser; the 532 nm light source used a Spectra-Physics Navigator X15SC-532Q frequency-doubled laser; and the 365 nm light source was provided by a Zolix MLED4-3 UV LED system. The electrical performance characterization of the devices was conducted using an Agilent B1500 A measurement system. Signal readout of photosynapse array was evaluated using a self-constructed electrical readout system.

Widespectrum fusion imaging platform and algorithm
An experimental platform was constructed utilizing double lasers operating at 532 nm (visible) and 10.6 μm (mid-infrared) wavelengths. A programmable motorized two-axis stage generated precise linear and curvilinear motion patterns to simulate vehicle movement trajectories. Atmospheric haze conditions were simulated by positioning a germanium filter along the optical path, which selectively attenuated visible wavelengths while maintaining infrared transmission. Image acquisition was implemented through a multiplexed column readout scheme with Field-Programmable Gate Array (FPGA)-controlled row-by-row sequential addressing at a frame rate of 12.5 fps (80 ms frame interval, 4k scale array). Precise temporal coordination between row addressing sequences and column multiplexing protocols was maintained to ensure signal integrity throughout the acquisition process. Following digitization, the data was transferred to the host computing system for subsequent analysis. The 532 nm (visible) and 10.6 μm (mid-infrared) images were fused using weighting coefficients of w(532 nm) = w(10.6 μm) = 0.5. For trajectory classification, a convolutional neural network (CNN) was trained on a dataset comprising terminal frames from linear and rotational movement sequences (20 images per category). The CNN consisted of three sequential convolutional blocks (8, 16, and 32 filters, respectively), each followed by batch normalization, ReLU activation, and max pooling operations. The network terminated with a fully connected layer and softmax classification layer. Training employed 5-fold cross-validation using stochastic gradient descent with momentum (SGDM) optimization and 500 training epochs. Performance was evaluated using validation accuracy metrics and confusion matrices.

Calculation of SSIM
The SSIM measures image similarity by:
SSIM(x,y) = ((2μ_x μ_y + C1)(2σ_xy + C2)) / ((μ_x^2 + μ_y^2 + C1)(σ_x^2 + σ_y^2 + C2)) (5)
where μ_x and μ_y are the means of images X and Y, σ_x and σ_y are their standard deviations, σ_xy is the covariance of images X and Y, and C1, C2 are non-negative regularization parameters that prevent divisional instability.

Data availability
Fusion image data used for comparison is pre-processed from the publicly available M3FD dataset69. All data supporting the key findings of this study are available within the article and its Supplementary Information files. Source data are provided with this paper. Additional raw data are available from the corresponding authors upon request. Source data are provided with this paper.

Code availability
The codes for plotting the data are available from the corresponding author upon request.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript entitled “Ultra-Wide Spectrum Photosynapse Array with 64k-Scale for Neuromorphic Fusion Imaging” by\nDun et al. presents a technical advance in the field of neuromorphic vision. The authors report the fabrication of a 64k-scale\nphotosynapse array utilizing a polydopamine-mediated process to achieve a device yield of 99.96%, addressing a\nscalability bottleneck in neuromorphic optoelectronic hardware. The use of CNT/MoO₃ heterojunctions enables a broadband\nspectral response, ranging from the ultraviolet (365 nm) to the long-wave infrared (10.6 μm), facilitating spatiotemporal data\nintegration directly at the sensor level. This hardware-level fusion removes the need for complex multi-sensor post-\nprocessing and enhances motion prediction accuracy and scene understanding. The manuscript is generally well structured,\nand the experimental results are convincing. However, I would recommend acceptance after addressing the following minor\nbut important technical clarifications and experimental validations.\n1. The authors report a high functional yield of 99.96% in the 64k-array, with only 23 pixels identified as non-functional.\nWhile this demonstrates excellent fabrication uniformity, further discussion of the failure analysis is necessary. Specifically,\nplease provide a spatial map or statistical analysis of the defect distribution. Are the defective pixels clustered in specific\nregions (e.g., edges, corners), or are they randomly distributed across the array? Such analysis could help identify process\nsensitivities or systematic fabrication deviations that could guide future yield optimization.\n2. In Figure 6e, the manuscript presents learning curves for recognition tasks. However, as training in neuromorphic systems\nis often sensitive to initial conditions, especially in hardware-accelerated platforms, it is important to assess the stability of\nconvergence. Please conduct and report recognition performance using multiple initial random weight configurations.\nPresent quantitative metrics such as standard deviation of final accuracy and number of epochs to convergence across\ntrials. These results can be included in the Supporting Information and would substantiate the robustness of the training\nprocess.\n3. The manuscript describes a dual-modal fusion imaging strategy integrating mid-infrared (MIR) and visible (Vis) signals. To\nfurther substantiate the benefit of adaptive modality fusion, please perform experiments varying the MIR:Vis weighting ratios\n(e.g., 0.3:0.7, 0.4:0.6, 0.6:0.4, 0.7:0.3). Report classification accuracy for motion patterns under each weighting condition, and\nprovide corresponding confusion matrices. This parametric analysis would offer valuable insight into the system’s tolerance\nto signal imbalance and help characterize the optimal fusion configuration.\n4. To enable a deeper understanding of the device physics and to facilitate comparisons with prior art, please provide\ndetailed responsivity data across the various wavelength range of interest. It is also important to specify the ambient or\ncontrolled temperature at which these measurements were conducted, as temperature can significantly influence the\nresponsivity of infrared and broadband photodetectors. Inclusion of this data—either in the main text or as Supporting\nInformation—would improve the reproducibility and interpretability of the results.\n5. The system is suitable for real-time autonomous perception. No data are provided on power consumption per pixel. For\nneuromorphic vision applications in autonomous systems, energy are key performance indicators.\n6. Only a few pulse-response traces are shown per wavelength, and decay behaviors are fitted with biexponential curves.\nHowever, statistical variability across multiple devices or pixels is not reported.\n7. The manuscript uses multiple phrases to describe the surface treatment process, including “polydopamine modification,”\n“PDA treatment,” and “polydopamine surface modification technique.” For clarity and consistency, please unify the\nterminology throughout the manuscript. If abbreviations are used (e.g., PDA), ensure that they are clearly defined at first\nmention and used uniformly thereafter. A standardized nomenclature would enhance the professional presentation and\nreadability of the work.", "reviewer_2": "The manuscript demonstrates a neuromorphic vision hardware consisting of a 64k-scale ultraviolet-to-mid-infrared (UV-to-\nMIR) photosynapse array based on CNT/MoO3 heterojunctions, which achieves 99.58% classification accuracy in trajectory\nrecognition through wide-spectrum neuromorphic fusion imaging. The development of large-scale neuromorphic arrays and\nbroadband imaging systems is of considerable significance for robust perception in complex environments. However, the\nmanuscript lacks in-depth mechanistic exploration of broadband response and synaptic dynamics, which limits its instructive\nvalue for the field. I encourage the authors to carefully address the following major concerns before I can support its\npublication.\n1. The authors report photoresponse non-uniformity (PRNU) only in the visible range. To support the claimed ultra-wide\nspectral responsivity, the PRNU characterization should be extended to UV, NIR, and MIR regions. This would provide a\nmore comprehensive understanding of device uniformity across the full operational spectrum.\n2. Unclear synaptic behavior under MIR stimulation (Figure 3b). Under repeated MIR pulses, the post-synaptic current\nexhibits inconsistent peaks (alternating high and low responses), which is inconsistent with typical synaptic facilitation\nbehavior. Detailed experimental parameters, including optical pulse intensity, duration, and number, should be clearly\nstated. Additionally, further analysis is needed to clarify the origin of the observed saturation behavior and whether it reflects\na true synaptic dynamic or a conventional photoconductive response.\n3. The manuscript lacks detailed experimental evidence to distinguish between photothermal and photoconductive\ncontributions in the MIR response of CNT. Given that mid-infrared radiation typically induces thermal effects, the authors\nshould perform control experiments (e.g., with modulated MIR sources, temperature monitoring, or thermal shielding) to\nverify whether the observed MIR response arises from photocarrier generation or thermal conductivity changes.\n4. Although the device is claimed to operate from UV to MIR, the authors only present static imaging demonstrations in the\nUV and visible ranges. The absence of NIR and MIR imaging experiments raises questions about the device performance in\nthese bands. Are there limitations in sensitivity that preclude demonstration in the infrared? Clarification is required.\n5. In Figure 6b, the visible-light trajectory is barely distinguishable from the background under the simulated haze\nenvironment. The authors should quantify the transmission of visible light through the attenuation medium used in the\nexperimental setup. Given the poor visibility in the visible range, it is plausible that MIR imaging alone could yield near-perfect classification accuracy, potentially diminishing the added value of spectral fusion.\n6. The authors should clarify the MIR transmittance of their ITO films and whether MIR-induced heating affects the electrical\nconductivity or contact properties of the device, potentially altering its responsivity.\n7. Polydopamine (PDA) is used as a surface modifier to improve film uniformity. The authors should clarify whether PDA\nexhibits any optical absorption in the UV-to-MIR range or contributes defect states at the CNT/MoO3 or interface that may\naffect the photosynaptic behavior.\n8. Figure 1a schematically illustrates the band alignment between CNT and MoO3 but does not provide direct experimental\nor theoretical support. The authors should indicate how this alignment was determined.\n9. The authors show double-exponential decay times (Figure 3l) that increase with wavelength. In particular, MIR responses\nexhibit much slower decay than UV–Vis. Is this due to different carrier dynamics or trap distributions? A more detailed\ndiscussion of wavelength-dependent dynamics is warranted, especially for synaptic applications requiring high-speed\nresponse.\n10. The manuscript lacks light-intensity-dependent responsivity data. This information is critical to evaluate linearity,\ndynamic range, and potential gain effects under different optical power levels across spectral bands. Such measurements\nwould also help distinguish synaptic accumulation from trap saturation effects.\n11. Given that thermal radiation from real-world targets at room temperature peaks near 10 μm, the authors should discuss\nwhether their array can effectively image dynamic thermal scenes without external illumination. Demonstrating passive MIR\ndetection at room temperature would significantly strengthen the practical impact of the work.", "reviewer_3": "The authors present a 64k-scale photosynapse array based on CNT/MoO3 heterojunctions for neuromorphic vision\napplications. The device demonstrates ultra-wide spectral response from UV to MIR and achieves high fabrication yield\nthrough polydopamine surface modification. The demonstrated improvements in trajectory prediction and recognition\naccuracy under challenging conditions are encouraging. The manuscript is generally well-organized and the experimental\nresults are comprehensive. I recommend this work for publication in Nature Communications with minor revisions as\ndetailed below:\n(1) The authors demonstrate that polydopamine treatment improves device uniformity. To further strengthen the discussion\non surface modification stability, it is recommended that the authors supplement a water contact angle measurement after\nstorage (e.g., after 30 days in ambient conditions).\n(2) The authors state that the Moran’s I index increases from 0.3010 (4k array) to 0.7613 (64k array), indicating enhanced\nspatial clustering in larger arrays. It is suggested the authors add a brief discussion on potential compensation strategies for\naddressing this issue, such as non-uniformity calibration algorithms.\n(3) The authors mention using a “queen contiguity configuration” for Moran’s I calculation without explanation. Please\nprovide a brief clarification of this spatial weight matrix configuration for readers unfamiliar with spatial statistics terminology.\n(4) The authors report achieving 99.96% pixel yield in the 64k array. It would be helpful if the authors could provide a spatial\nmap or brief description of the non-functional pixel distribution to understand whether defects are randomly distributed or\nshow clustering patterns.\n(5) In Figure 4 caption, there appears to be a typographical error. “365 nm visible light” and “532 nm UV light” should be\nrevised.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_5": "The authors present a wide-spectrum photosynapse array based on CNT/MoO3 heterojunction with the largest\ndemonstrated array size and response spectrum. A compelling case for wide response spectrum is made with side-by-side\ncomparison of a glass bottle containing a metal ring that is imaged using UV light and visible light. Both imaging results are\nshown to contain important semantic information (either exterior morphology or metallic ring). The presented photosynapse\narray shows strong potential for realizing a wide-spectrum combined sensing + processing system applied to\nlearning/robotics.\nThe following comments and questions are provided with regard to this work:\n1. In Line 48-51, authors mention that visible spectrum + CMOS sensors perform poorly in adverse visibility conditions.\nCurrently, dynamic vision sensors (DVS), which transmit sparse, event-driven spikes, also offer heightened computer vision\n(CV) performance in challenging conditions, like fog and smoke, due to higher temporal resolution, reduced motion blur, and\nlogarithmic light sensitivity (and therefore high dynamic range) when compared to RGB cameras. See\nhttps://arxiv.org/pdf/1904.08405 and https://arxiv.org/pdf/2404.10540 for more details. Additionally, due to DVS emulating the\nretina, they are well-suited to integrate into neuromorphic systems that aim to unite brain-like computation and brain-like\nsensory peripherals. Lastly, there is an established community of neuromorphic CV and robotics engineers that have\ndesigned and optimized software packages, like LavaDL (for Loihi 2 simulation/programming) and Neuromorphic\nIntermediate Representation (connecting LavaDL, snnTorch, SpiNNaker2, Norse, etc.), for a streamlined DVS to\ncomputational hardware to learning/robotics pipeline. How does the proposed photosynapse array compare with DVS/event\ncameras? Specifically, what tasks would the proposed system outperform an RGB + DVS system and what is the\nfrequency/criticality of such tasks (e.g., in the context of autonomous vehicles)?\n2. I can see how the proposed photosynapse array has strong potential to simplify existing sensing/computing systems.\nHowever, I would like to see a clearer connection made here. Namely, existing autonomous systems use a suite of sensors,\nincluding LiDAR, radar, RGB camera, IR/thermal. What portion of this sensor suite would the proposed system simplify?\n3. In Line 191, a Moran’s I index of 0.3010 for dark current is mentioned. This suggests there is some spatial correlations in\nthe visual scene. Is this from some fixed-pattern noise, thermal leakage, or electrical crosstalk between nearby devices?\nAdditionally, in Line 197, was the higher Moran’s I index of 0.7613 also in dark conditions? Am I correct in my understanding\nthat Moran’s I index should be close to zero in dark conditions? If not, could this be clarified more in the manuscript?\n4. In Line 220-222, it is stated that the frequency-dependent photoresponse (i.e., the extent of synaptic relaxation/leakage)\nmimics the temporal filtering properties of biological neural systems, allowing neural circuits to selectively respond to\nspecific temporal frequencies of input signals. It is very unclear how this is possible. My best guess is that synaptic\ndecay/Laplace kernels act as low-pass filters (e.g., a Lorentzian-style frequency response) which allow only certain\nfrequencies to pass. But these are not bandpass and thus would not respond to specific temporal frequencies with great\nprecision. Can this be explained more?\n5. Lines 281-288 mention the advantage of each frame encoding a time-weighted history of motion trajectories. This is\nstandard in all DVS + neuromorphic vision systems. See Fig. 2 in \"HOTS: A Hierarchy of Event-Based Time-Surfaces for\nPattern Recognition\" (https://ieeexplore.ieee.org/document/7508476) for an example. This is a direct benefit of any system\nthat appends an exponential decay kernel to visual events and is not a novel/unexpected result presented by this work.\nLearning systems that train on DVS hardware data (with exponential decay kernel circuits or similar downstream processing\nin software) are highly competent at next-frame prediction of complex trajectories, required, for example, in optical flow tasks.\n6. For the last section, titled “Demonstration of neuromorphic fusion imaging,” it is likely that a DVS-only or DVS + RGB input\nwould yield a better result than the provided conventional imaging hardware result. It would be a fairer comparison since\nDVS hardware data: (1) is better suited for hazy conditions and (2) can have built-in exponential decay kernel circuitry that\npreserves temporal history of events. Furthermore, different sensors require different learning models. So it would be more\nfair to use HOTS or EVFlowNet (https://arxiv.org/abs/1802.06898) -- with an equivalent number of parameters as the CNN\nused for the photosynapse array hardware -- for the DVS hardware data. In this way, the learning models would be of\ncomparable size/capacity, incurring comparable energy consumption, but would be tailored to their specific input data."}, "author_response": "Point-by-Point Response Letter\nWe would like to first thank the Editor and the Reviewers for careful reading of\nour manuscript and for their valuable comments and suggestions. We are truly grateful\nand encouraged by these positive remarks. On the other hand, following the Reviewers’\nother suggestions, we have significantly improved both the content and the quality of\nour work. Here we would like to carefully respond to the Reviewers’ questions and\nconcerns point by point.\nReviewer #1\nThe manuscript entitled “Ultra-Wide Spectrum Photosynapse Array with 64k-\nScale for Neuromorphic Fusion Imaging” by Dun et al. presents a technical advance in\nthe field of neuromorphic vision. The authors report the fabrication of a 64k-scale\nphotosynapse array utilizing a polydopamine-mediated process to achieve a device\nyield of 99.96%, addressing a scalability bottleneck in neuromorphic optoelectronic\nhardware. The use of CNT/MoO heterojunctions enables a broadband spectral\n3\nresponse, ranging from the ultraviolet (365 nm) to the long-wave infrared (10.6 μm),\nfacilitating spatiotemporal data integration directly at the sensor level. This hardware-\nlevel fusion removes the need for complex multi-sensor post-processing and enhances\nmotion prediction accuracy and scene understanding. The manuscript is generally well\nstructured, and the experimental results are convincing. However, I would recommend\nacceptance after addressing the following minor but important technical clarifications\nand experimental validations.\nResponse: We are grateful for the reviewer’s encouraging assessment and\ninsightful recommendations. We have conducted additional experiments and performed\nfurther analyses to address each of the points raised, thereby enhancing the quality of\nour manuscript. Detailed responses to all comments are presented point by point below.\n1. The authors report a high functional yield of 99.96% in the 64k-array, with only\n23 pixels identified as non-functional. While this demonstrates excellent fabrication\nuniformity, further discussion of the failure analysis is necessary. Specifically, please\nprovide a spatial map or statistical analysis of the defect distribution. Are the defective\n1\npixels clustered in specific regions (e.g., edges, corners), or are they randomly\ndistributed across the array? Such analysis could help identify process sensitivities or\nsystematic fabrication deviations that could guide future yield optimization.\nResponse: Thank you for your valuable suggestion. We have conducted a detailed\nstatistical analysis of the spatial distribution of the 23 non-functional pixels, and the\nresults are presented in the added Figure R1. The analysis reveals a clear spatial\nclustering pattern: all non-functional pixels are concentrated in the corner regions of\nthe array. This edge-concentrated failure mode is consistent with edge effects\ncommonly observed in large-scale array fabrication (ACS Applied Materials &\nInterfaces, 2022, 14(37): 42365-42373.; Acta Materialia, 2001, 49(1): 93-107.). In our\ncase, the insufficient spacing between electrode pads and the active array allows the\nphotoresist edge effects to extend into the active region. The step discontinuities formed\nduring pad protection affect PDA deposition uniformity at array corners (Journal of\nApplied Physics, 1998, 84(7): 3636-3649.). Future potential improvements include\noptimized peripheral routing topology to minimize edge effect, combined with adaptive\nnon-uniformity correction (NUC) algorithms employing two-point calibration or scene-\nbased statistical methods to compensate for residual fixed-pattern noise (IEEE\nTransactions on Image Processing, 2000, 8(8), 1148-1151.; Optical Engineering,\n1985, 24(5), 855-862.).\nWe have incorporated this spatial distribution analysis (Figure S10) and\ndiscussion into the revised manuscript.\nphotocurrent (I =I -I ) values. (b) Enlarged view of locations for non-\neff photo photo dark\nfunctional pixels.\nRevision.\nThe Moran’s I indices of 0.3010 (64×64 array) and 0.7613 (256×256 array)\nmeasured under dark conditions indicate spatial correlations in dark current distribution,\nwhich can be primarily attributed to fixed-pattern noise from fabrication-induced edge\neffects (see Note S3 for detailed analysis). These edge effects caused by topographical\nvariations where interconnects route to peripheral pads, become more pronounced in\nlarger arrays, as supported by the spatial clustering of failed pixels in edge regions\n(Figure S10). Future potential improvements include optimized peripheral routing\ntopology to minimize edge effect, combined with adaptive non-uniformity correction\n(NUC) algorithms employing two-point calibration or scene-based statistical methods\nto compensate for residual fixed-pattern noise 47,48.\nReferences.\n47. Harris, J. G. & Yu-Ming Chiang. Nonuniformity correction of infrared image\nsequences using the constant-statistics constraint. IEEE Trans. Image Process. 8, 1148–\n1151 (1999).\n48. Milton, A. F., Barone, F. R. & Kruer, M. R. Influence Of Nonuniformity On\nInfrared Focal Plane Array Performance. Opt. Eng. 24, (1985).\nNote S3. Analysis of spatial distribution of non-functional pixels.\nThe spatial distribution analysis (Figure S10) reveals that all 23 non-functional\npixels are concentrated in the array corners. This edge-concentrated failure mode may\nbe attributed to insufficient spacing between electrode pads and the active array, which\nallows photoresist edge effects to extend into the active region. The step discontinuities\nformed during pad protection specifically affect PDA deposition uniformity at array\ncorners. Potential improvements could include implementing dedicated corner\nstructures, extending the guard ring design into corner regions, or incorporating dummy\npixels at the array periphery to shield active pixels from edge-related variations.\n3\n2. In Figure 6e, the manuscript presents learning curves for recognition tasks.\nHowever, as training in neuromorphic systems is often sensitive to initial conditions,\nespecially in hardware-accelerated platforms, it is important to assess the stability of\nconvergence. Please conduct and report recognition performance using multiple initial\nrandom weight configurations. Present quantitative metrics such as standard deviation\nof final accuracy and number of epochs to convergence across trials. These results can\nbe included in the Supporting Information and would substantiate the robustness of the\ntraining process.\nResponse: We thank the reviewer for this insightful suggestion. We have\nconducted additional experiments to assess the stability of recognition performance\nacross different random weight initializations. As shown in the supplementary Figure\nR2, we performed 10 independent training runs with different random weight\nconfiguration initializations, where the convergence epoch varied from 40 to 120\nepochs. The final recognition accuracy showed good consistency (99.75% ± 0.75%)\nacross all trials, indicating that images captured by our photosynapse array support\nstable classification performance. These results suggest that while the convergence\nspeed varies with initialization, the final performance remains stable. This discussion\nhas been added to the Main text and Figure S25.\nFigure R2 (Figure S25 in revised manuscript). Stability analysis across different\nrandom weight initializations. (a) Final recognition accuracy for 10 independent\ntraining runs. (b) Convergence epochs required for each run.\nRevision.\n10 independent training runs with different random weight initializations (Supporting\nInformation, Figure S25). The consistent high accuracy (99.75% ± 0.75%) suggests\nthat images captured by our photosynapse array support stable classification\nperformance.\n3. The manuscript describes a dual-modal fusion imaging strategy integrating mid-\ninfrared (MIR) and visible (Vis) signals. To further substantiate the benefit of adaptive\nmodality fusion, please perform experiments varying the MIR:Vis weighting ratios\n(e.g., 0.3:0.7, 0.4:0.6, 0.6:0.4, 0.7:0.3). Report classification accuracy for motion\npatterns under each weighting condition, and provide corresponding confusion matrices.\nThis parametric analysis would offer valuable insight into the system’s tolerance to\nsignal imbalance and help characterize the optimal fusion configuration.\nResponse: We thank the reviewer for this valuable suggestion. We conducted\nexperiments with varying MIR:Vis weighting ratios from 0.1:0.9 to 1:0 (Figure R3 and\nR4). The results show that classification accuracy increases dramatically from 74.25%\n(at 0.1:0.9) to 91.75% (at 0.2:0.8). The system maintains high accuracy (>98%) across\nfusion ratios from 0.3:0.7 to 1:0, demonstrating robust performance and tolerance to\ndifferent weighting configurations. Detailed results and confusion matrices have been\nadded to the Figure S26 and S27 in Supporting Information, and the discussion have\nbeen added the in main text.\nRevision.\nFurther analysis of different MIR:Vis fusion ratios showed that classification\nperformance improved with increasing MIR contribution and stabilized at high\naccuracy levels, confirming the robustness of the fusion approach(Figure S26 and 27).\nMIR/visible weight ratios. Error bars represent standard deviation over 10 independent\nruns.\nFigure R4 (Figure S27 in revised manuscript). Confusion matrices at different\nMIR:Vis weight ratios. Values are normalized prediction probabilities.\n4. To enable a deeper understanding of the device physics and to facilitate\ncomparisons with prior art, please provide detailed responsivity data across the various\nwavelength range of interest. It is also important to specify the ambient or controlled\ntemperature at which these measurements were conducted, as temperature can\nsignificantly influence the responsivity of infrared and broadband photodetectors.\nInclusion of this data—either in the main text or as Supporting Information—would\nimprove the reproducibility and interpretability of the results.\ncomprehensive characterization of device responsivity. We have conducted detailed\nresponsivity measurements across the wavelengths (Figure R5). All measurements\nwere performed at a temperature of 26°C in a shielded condition to suppress\nelectromagnetic interference and environmental thermal disturbances. The\nmeasurements suggest that our photosynapse device maintains stable responsivity\nacross UV to MIR wavelengths.\nFigure R5 (Figure S15 in revised manuscript). Wavelength-dependent responsivity\ncharacteristics of the photosynapse device. Responsivities were measured at\nwavelengths of (a) 365 nm, (b) 532 nm, (c) 1064 nm, and (d) 10.6 μm under various\noptical power densities. Test conditions: 2 s illumination duration, 0.5 V bias, 26°C.\nRevision.\nDetailed wavelength-dependent responsivity measurements demonstrate effective\nwide-spectrum photoresponse from UV to MIR range (Figure S15).\n7\n5. The system is suitable for real-time autonomous perception. No data are\nprovided on power consumption per pixel. For neuromorphic vision applications in\nautonomous systems, energy are key performance indicators.\nResponse: We thank the reviewer for pointing out the importance of power\nconsumption metrics. Based on our measured dark current and photocurrent, we\ncalculate the power consumption per pixel as follows: Under our operating bias voltage\nof 0.5 V, we measured a dark current of 34.4 nA and a photocurrent of 61.3 nA under\n6 mW cm-2 illumination at 1064 nm. Therefore: (1) Static power consumption (dark\nstate): P = 0.5 V × 34.4 nA = 17.2nW per pixel (2) Dynamic power consumption\ndark\n(illuminated): P = 0.5 V ×61.3nA =30.65 nW per pixel (3) For operation with a\nlight\ntypical 50% illumination duty cycle, the average power consumption is: P = 17.2 nW\navg\n× 0.5 + 30.65 nW × 0.5 = 23.9 nW per pixel.\nThese values indicate that our device achieves low power consumption for\nautonomous perception applications. We have added this power consumption\nestimation to the Note S8. in revised manuscript.\nRevision.\nThe device exhibits low power consumption of 23.9 nW per pixel at 0.5 V bias\n(Supporting Information Note S8), demonstrating potential for energy-efficient\nperception systems.\nNote S8. Estimation of power consumption for per pixel.\nUnder our operating bias voltage of 0.5 V, we measured a dark current of 34.4 nA\nand a photocurrent of 61.3 nA under 6 mW cm-2 illumination at 1064 nm. Therefore:\n(1) Static power consumption (dark state): P = 0.5 V × 34.4 nA = 17.2nW per pixel\ndark\n(2) Dynamic power consumption (illuminated): P = 0.5 V ×61.3nA =30.65 nW per\nlight\npixel (3) For operation with a typical 50% illumination duty cycle, the average power\nconsumption is: P = 17.2 nW × 0.5 + 30.65 nW × 0.5 = 23.9 nW per pixel.\navg\n8\n6. Only a few pulse-response traces are shown per wavelength, and decay\nbehaviors are fitted with biexponential curves. However, statistical variability across\nmultiple devices or pixels is not reported.\nResponse: We thank the reviewer for this important comment. We performed\ndevice-to-device (D2D) uniformity analysis on five spatially distributed devices from\nthe same batch. Figure R6 presents photoresponse traces from five pixels at different\nspatial locations, measured at four wavelengths (365 nm, 532 nm, 1064 nm, and 10.6\nμm) under identical illumination conditions. As shown in Figure R7, the biexponential\nfitting reveals good reproducibility across fabricated pixels. These results have been\nadded to Figure S17 and Figure 3l in revised manuscript.\nRevision.\nDevice-to-device uniformity measurements demonstrate consistent photosynapse\nbehavior (Figures 3l and S17).\nFigure R6 (Figure S17 in revised manuscript). Device-to-device uniformity study.\nResponse traces from five pixels at (a) 10.6 μm, (b) 1064 nm, (c) 532 nm, ad (d) 365\nnm, showing consistent temporal behavior across different devices.\ntime constants statistics. τ and τ values represent averages from five pixels, with error\n1 2\nbars showing statistical spread.\n7. The manuscript uses multiple phrases to describe the surface treatment process,\nincluding “polydopamine modification,” “PDA treatment,” and “polydopamine surface\nmodification technique.” For clarity and consistency, please unify the terminology\nthroughout the manuscript. If abbreviations are used (e.g., PDA), ensure that they are\nclearly defined at first mention and used uniformly thereafter. A standardized\nnomenclature would enhance the professional presentation and readability of the work.\nResponse: We thank the reviewer for this careful observation regarding\nterminology consistency. We have unified the terminology throughout the manuscript.\nWe have used “polydopamine (PDA) modification” at first mention and “PDA\nmodification” consistently thereafter.\nThe manuscript demonstrates a neuromorphic vision hardware consisting of a 64k-\nscale ultraviolet-to-mid-infrared (UV-to-MIR) photosynapse array based on\nCNT/MoO3 heterojunctions, which achieves 99.58% classification accuracy in\ntrajectory recognition through wide-spectrum neuromorphic fusion imaging. The\ndevelopment of large-scale neuromorphic arrays and broadband imaging systems is of\nconsiderable significance for robust perception in complex environments. However, the\nmanuscript lacks in-depth mechanistic exploration of broadband response and synaptic\ndynamics, which limits its instructive value for the field. I encourage the authors to\ncarefully address the following major concerns before I can support its publication.\nResponse: We sincerely thank the reviewer for the insightful and constructive\ncomments. Following the reviewer’s valuable suggestions regarding the need for\ndeeper mechanistic exploration, we have conducted important additional experiments\nto investigate both the wide-spectrum response mechanisms and synaptic dynamics.\nWe hope these new insights help address the reviewer’s concerns and enhance the\nmanuscript’s contribution. We provide detailed point-by-point responses to each\nspecific comment below.\n1. The authors report photoresponse non-uniformity (PRNU) only in the visible\nrange. To support the claimed ultra-wide spectral responsivity, the PRNU\ncharacterization should be extended to UV, NIR, and MIR regions. This would provide\na more comprehensive understanding of device uniformity across the full operational\nspectrum.\nResponse: We thank the reviewer for this valuable suggestion. Following the\nreviewer’s recommendation, we have extended the PRNU characterization to cover the\nextended ranges (365 nm UV, 1064 nm NIR, and 10.6 μm MIR). For the UV ranges,\nwe performed standard PRNU measurements using uniform illumination sources,\nwhich yielded a PRNU value of 27.3%. For the NIR and MIR ranges, large scale\nuniform illumination laser sources are difficult to obtain, we therefore adopted a point-\nsource scanning methodology (Appl. Opt. 2017, 56, 5972-5977) to evaluate the PRNU.\n30.2% for the MIR region (10.6 μm). These results are summarized in Table S2 and\ndetailed discussions have been included in the Supporting Information Note S4.\nTable S2. Photoresponse non-uniformity (PRNU) results at different wavelengths.\nWavelength PRNU Characterization Method\n365 nm(6.0 mW cm-2) 27.3% Uniform illumination\n532 nm(6.0 mW cm-2) 27.7% Uniform illumination\n1064 nm(6.0 mW cm-2) 29.4% Point-source scanning\n10.6 μm(56.6 mW cm-2) 30.2% Point-source scanning\nRevision.\nThe corresponding PRNU values at 365 nm, 1064 nm, and 10.6 μm were 27.3%,\n29.4%, and 30.2%, respectively (see Table S2 and Note S4 for details).\nNote S4. The wavelength-dependent uniformity analysis.\nThe wavelength-dependent uniformity characterization revealed the spatial\nresponse characteristics. The UV/visible regions showed relatively lower PRNU values\n(UV: 27.3%, visible: 27.7%), while the NIR and MIR regions exhibited higher spatial\nnon-uniformity (NIR: 29.4%, MIR: 30.2%). The NIR region (1064 nm) showed\nincreased non-uniformity (PRNU = 29.4%), possibly due to greater penetration depth\nthat could enhance sensitivity to bulk thickness and compositional variations. The MIR\nregion (10.6 μm) displayed the non-uniformity of 30.2%, potentially influenced by\nthermal effects becoming more significant at this wavelength20. The higher PRNU\nvalues in NIR/MIR regions may also be partially attributed to the point-source scanning\nmethod, which could introduce additional positioning uncertainties compared to\nUV/Vis uniform illumination. Despite these methodological differences, the\nexperiments demonstrate the large-scale photoresponse across the ultra-wide spectral\nrange, supporting our broadband photodetection capability.\n12\n2. Unclear synaptic behavior under MIR stimulation (Figure 3b). Under repeated\nMIR pulses, the post-synaptic current exhibits inconsistent peaks (alternating high and\nlow responses), which is inconsistent with typical synaptic facilitation behavior.\nDetailed experimental parameters, including optical pulse intensity, duration, and\nnumber, should be clearly stated. Additionally, further analysis is needed to clarify the\norigin of the observed saturation behavior and whether it reflects a true synaptic\ndynamic or a conventional photoconductive response.\nResponse: We thank the reviewer for this insightful observation regarding the\ninconsistent peak currents in the MIR synaptic response. We acknowledge that the\namplitude fluctuations observed in the original Figure 3b required clarification.\nFirst, we provide the detailed experimental parameters: the MIR illumination was\nperformed at 56.6 mW cm-2 intensity with 10 consecutive pulses of 2s duration and 2s\nintervals (ON/OFF = 2s/2s). The amplitude fluctuations in the original MIR\nmeasurement can be attributed to environmental thermal noise and background\nradiation. The susceptibility to this noise is inherently more pronounced for room-\ntemperature MIR detection compared to shorter wavelengths (Infrared and Terahertz\nDetectors, 2019, CRC Press. 3rd ed.)\nTo address this issue, we implemented comprehensive electromagnetic shielding\nand proper grounding of the measurement setup. After these improvements, as shown\nFigure R8, the MIR synaptic responses demonstrate clear and consistent facilitation\nbehavior with monotonic current accumulation across successive pulses, confirming\nthe reliability of our synaptic devices.\nTo further distinguish the observed saturation from a conventional\nphotoconductive effect, we conducted frequency-dependent experiments at pulse rates\nof 0.25 Hz (2 s ON/2 s OFF), and 0.5 Hz (1 s ON/1 s OFF), as shown in Figure R8.\nThe results clearly demonstrate that higher stimulation frequencies lead to more\npronounced current accumulation corresponding to synaptic short-term plasticity\n(Advanced Materials, 2019, 31(15), 1806364). This behavoirs are different from\nconventional photodetectors (Physics of Semiconductor Devices, 2007, Hoboken, NJ,\nwith the saturation behavior reflecting the finite capacity of trap states in MoO ,\n3\nsupporting that our devices exhibit synaptic dynamics rather than a simple\nphotoconductive response. We have updated Figure 3b and added the Figure S11 in\nthe revised manuscript with these improvements.\nFigure R8 (Figure 3b and S11 in revised manuscript). Frequency-dependent post-\nsynapse current response under 10.6 μm MIR stimulation. The device was subjected to\nthree consecutive pulse trains with different frequencies and corresponding ON/OFF\ntimes: 0.25 Hz (2 s ON/2 s OFF, Figure 3b), and 0.5 Hz (1 s ON/1 s OFF, Figure S11),\neach consisting of 10 pulses with 50% duty cycle. Experimental parameters: optical\npower density = 56.6 mW cm-2, device area = 0.04 mm2, bias voltage = 0.5 V,\ntemperature = 26°C.\nRevision.\nWhen the stimulation frequency increases to 0.5 Hz (ON/OFF = 1s/1s, Figure S11),\nthe short inter-pulse intervals prevent complete current decay between pulses, resulting\nin accumulation of successive current responses. This photoresponse mimics the\ntemporal filtering properties of biological neural systems, where the synaptic decay\ncreates a temporal integration window that enables frequency-dependent signal\nprocessing52. Such behavior differs from conventional photoconductive devices, which\ntypically exhibit static photoresponse without history-dependent modulation.\n14\n3. The manuscript lacks detailed experimental evidence to distinguish between\nphotothermal and photoconductive contributions in the MIR response of CNT. Given\nthat mid-infrared radiation typically induces thermal effects, the authors should perform\ncontrol experiments (e.g., with modulated MIR sources, temperature monitoring, or\nthermal shielding) to verify whether the observed MIR response arises from\nphotocarrier generation or thermal conductivity changes.\nResponse: We thank the reviewer for this valuable comment regarding the MIR\nresponse mechanism. To address this concern, we have performed systematic\nexperiments to differentiate between photocarrier generation and thermally-induced\nconductivity changes.\nFirst, we quantified the contribution from thermally-induced conductivity changes.\nAn infrared thermal camera measurement under continuous MIR illumination (10.6 μm,\n56.6 mW cm-2) revealed a local temperature increase of 2.8 K in the device (Figure\nR9). A subsequent control experiment using a thermal stage showed that this 2.8 K\ntemperature rise induces a current of ~1.2 nA (Figure R10), while the total\nphotocurrent under the illumination was 4.9 nA. This indicates that thermally-induced\nconductivity changes account for ~24.5% of the total photoresponse.\nTo determine the influence of thermal effects on synaptic behavior, we employed\na pulsed successive illumination experiment with simultaneous current and temperature\nmonitoring. The results demonstrate a characteristic accumulative increase in\nphotocurrent over a train of 10 MIR pulses (from 4.9 nA for the first pulse to 7.5 nA\nfor the tenth pulse, Figure R11). In contrast, the device temperature remains relatively\nstable throughout the entire pulse sequence (Figure R11). This clear decoupling\nbetween the accumulating photocurrent and the stable temperature conclusively suggest\nthat the observed synaptic plasticity is mainly governed by photoelectric process rather\nthan thermally-induced conductivity changes.\nBased on these experiments, we propose that the MIR response comprises two\ncomponents: (i) a baseline thermal contribution (~24.5%) from temperature-induced\nconductivity changes, and (ii) the photoelectric process responsible for synaptic\n15\nplasticity. The latter involves MIR photon absorption by CNTs, followed by electron\ntransfer to MoO and subsequent trapping at oxygen vacancy states. The decoupling of\n3\naccumulating photocurrent from stable device temperature confirms that synaptic\nfunctionality arises from this photocarrier generation and trapping mechanism rather\nthan thermal effects. This detailed analysis has been incorporated into the revised\nmanuscript.\nThe manuscript has been revised to incorporate these results and discussions.\nFigure R9 (Figure S19 in revised manuscript). Photothermal characterization using\ninfrared thermography. (Left) Baseline temperature distribution at 26.0 °C. (Right)\nTemperature profile under MIR illumination (10.6 μm, 56.6 mW cm-2) showing a\n2.8 °C temperature rise at the illumination spot.\nFigure R10 (Figure S20 in revised manuscript). Thermal control experiment\nshowing current change induced by 2.8°C heating from 26.0°C to 28.8°C via hot plate.\nThe ~1.2 nA current increase indicates the thermal contribution of total current.\nevolution during 10 consecutive MIR pulses. The photocurrent shows an accumulative\nincrease with pulse number while the device temperature remains stable throughout the\nmeasurement.\nRevision.\nSince MIR radiation can induce thermal effects through absorption in device\ncomponents including active materials, substrate, and ITO electrodes (Figure S19), we\nconducted systematic experiments to investigate the contribution of thermally-induced\nconductivity changes for synapse behaviors. Thermal imaging of the entire device\nunder operating conditions revealed a temperature rise of 2.8 K during MIR\nillumination, contributing ~1.2 nA to the total photocurrent. (Figure S20). Pulse-train\nexperiments support that synaptic plasticity originates from photocarrier trapping rather\nthan thermal accumulation (Figure S21), with stable ohmic contacts maintained within\nthe temperature range. Detailed analysis of these thermal effects is provided in Note S7.\nNote S7. Investigation of thermal effects contributions in MIR photosynapse\nbehaviors.\nInfrared thermal imaging showed that MIR illumination (10.6 μm, 56.6 mW cm-2,\n2s duration) induces a total temperature rise of 2.8 K across entire device components\n(Figure S19), corresponding to a photocurrent of 4.9 nA. Control experiments using a\nthermal stage revealed that this 2.8 K increase generates ~1.2 nA of current (Figure\nS20). To further investigate the origin of synaptic behavior, we applied pulsed MIR\nillumination while monitoring both photocurrent and temperature. The photocurrent\n17\nexhibited characteristic accumulative enhancement over 10 pulses (from 4.9 nA for the\nfirst pulse to 7.5 nA for the tenth pulse, Figure S21), while the device temperature\nremained stable throughout (Figure S21). This decoupling between accumulating\nphotocurrent and stable temperature supports that synaptic plasticity is predominantly\ngoverned by photoelectric processes. Furthermore, I-V measurements confirmed that\nthe ohmic characteristics are maintained within the temperature range (Figure S18).\n4. Although the device is claimed to operate from UV to MIR, the authors only\npresent static imaging demonstrations in the UV and visible ranges. The absence of\nNIR and MIR imaging experiments raises questions about the device performance in\nthese bands. Are there limitations in sensitivity that preclude demonstration in the\ninfrared? Clarification is required.\nResponse: We thank the reviewer for this valuable suggestion. We have\nsupplemented our imaging demonstrations with NIR and MIR experiments. Since NIR\n(1064 nm) and MIR (10.6 μm) sources typically provide focused beams rather than the\nwide-field illumination available for UV/Vis, we employed a raster-scanning approach\nto generate images in these spectral regions (Appl. Phys. Lett. 2002, 80, 3265–3267).\nA programmable XY translation stage was utilized to scan the focused laser spot\nacross the device’s active area (scanning path shown in Figure R12). To ensure the\nfidelity of the resulting image, a step-and-measure acquisition was implemented. This\nprotocol incorporates a sufficient dwell time at each pixel to mitigate potential artifacts\narising from the device’s synaptic memory, ensuring that each measurement\ncorresponds to an independent spatial point. The acquired NIR and MIR images in the\nrevised Figure S22 demonstrate well-resolved patterns, supporting the device’s\nimaging functionality across the wide spectrum. The manuscript has been revised to\nincorporate these results and discussions.\n10.6 μm MIR and (b) 1064 nm NIR imaging of N patterns. Left panels show the\nscanning path and right panels display the reconstructed images at both wavelengths.\nRevision.\nFurthermore, raster-scanning imaging in the near-infrared (1064 nm) and mid-\ninfrared (10.6 μm) regions yielded well-resolved patterns (Figure S22), confirming the\narray’s imaging functionality across the ultra-wide spectrum range.\n5. In Figure 6b, the visible-light trajectory is barely distinguishable from the\nbackground under the simulated haze environment. The authors should quantify the\ntransmission of visible light through the attenuation medium used in the experimental\nsetup. Given the poor visibility in the visible range, it is plausible that MIR imaging\nalone could yield near-perfect classification accuracy, potentially diminishing the\nadded value of spectral fusion.\nResponse: We appreciate the reviewer’s careful observation regarding the low\nvisibility of the trajectory in Figure 6b. Following this valuable suggestion, we have\nquantified the transmission characteristics of our experimental setup. The attenuation\nmedium, consisting of a 50 nm Ge thin film deposited on a polyethylene substrate,\n19\nprovides ~3% transmission at 532 nm while maintaining ~40% transmission at 10.6 μm,\nwhich represent a simplified extreme low visibility weather condition.\nTo address the reviewer’s concern about the added value of visible light, we\nperformed a comprehensive evaluation of our wide-spectrum fusion system’s\nperformance across diverse atmospheric conditions using the well-established M3FD\ndataset (In Proceedings of the IEEE/CVF Conference on Computer Vision and\nPattern Recognition (CVPR) 2022, 5800-5809) (detailed in Note S10), which captures\nrich real-world autonomous driving scenarios, providing practical insights into system\nperformance in actual deployment environments.\nAs shown in the Figure R13, our analysis covers visibility conditions ranging\nfrom 1 km to 10 km (Optics express, 2011, 19(4): 3379-3386.). The results demonstrate\nthat while MIR imaging indeed provides the dominant contribution under severe\nattenuation conditions, the wide-spectrum fusion approach consistently enhances\nclassification accuracy across all visibility conditions. Even in heavy fog (1 km\nvisibility), where visible-light performance is severely degraded to ~50% accuracy,\nfusion maintains improvement over MIR-only imaging. The results suggest the\ncontribution of visible-light features even under atmospheric attenuation\nFigure R13 (Figure S28 in revised manuscript). Performance evaluation of wide-\nspectrum fusion under different visibility conditions. (a) Atmospheric transmittance\ncurves for 532 nm visible and 10.6 μm MIR wavelengths as a function of visibility. (b)\nRecognition accuracy comparison of visible-only, MIR-only, and fusion approaches at\n20\nvisibility distances of 1 km, 10 km, and 100 km, demonstrating improvement with\nwide-spectrum fusion across various conditions.\nRevision.\nTo further evaluate the contribution of visible-light fusion under various\natmospheric conditions, we analyzed our system’ performance using the M3FD dataset\nwith visibility-dependent attenuation based on established atmospheric transmission\nmodels72,73. The results suggest that wide-spectrum fusion consistently enhances\nclassification accuracy compared to MIR-only imaging across different visibility\nconditions, suggesting the contribution of visible-light features even under atmospheric\nattenuation (Figure S28).\nReferences.\n72. Liu, J. et al. Target-aware Dual Adversarial Learning and a Multi-scenario\nMulti-Modality Benchmark to Fuse Infrared and Visible for Object Detection. in 2022\nIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 5792–\n5801 (IEEE, New Orleans, LA, USA, 2022). doi:10.1109/CVPR52688.2022.00571.\n73. Grabner, M. & Kvicera, V. The wavelength dependent model of extinction in\nfog and haze for free space optical communication. Opt. Express 19, 3379 (2011).\nNote S10. Wide-spectrum Fusion Performance Analysis Under Various\nAtmospheric Conditions.\nWe evaluated wide-spectrum fusion performance using the real-world M3FD\ndataset with visibility-dependent attenuation based on atmospheric transmission\nmodel2324. This model captures the reduced scattering of longer infrared wavelengths\ncompared to visible light in fog conditions. (Figure S28a) The M3FD dataset was\nprocessed to simulate visibility conditions from 1 to 10 km by applying wavelength-\nspecific attenuation to visible and MIR channels separately. Each pixel intensity was\nscaled by the corresponding transmission coefficient while preserving ground truth\nlabels. We evaluated classification accuracy for visible-only, MIR-only, and fusion\nconfigurations across all visibility conditions. As shown in Figure S28b, the results\nshow that wide-spectrum fusion consistently outperforms compared with visible-only,\nvisible-light accuracy degrades severely, fusion maintains superior performance\ncompared to MIR-only imaging, indicating the complementary value of both spectral\nchannels in adverse conditions.\nSI references.\n23. Liu, J. et al. Target-aware Dual Adversarial Learning and a Multi-scenario\nMulti-Modality Benchmark to Fuse Infrared and Visible for Object Detection. in 2022\nIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 5792–\n5801 (IEEE, New Orleans, LA, USA, 2022). doi:10.1109/CVPR52688.2022.00571.\n24. Grabner, M. & Kvicera, V. The wavelength dependent model of extinction in\nfog and haze for free space optical communication. Opt. Express 19, 3379 (2011).\n6. The authors should clarify the MIR transmittance of their ITO films and whether\nMIR-induced heating affects the electrical conductivity or contact properties of the\ndevice, potentially altering its responsivity.\nResponse: We thank the reviewer for this important question about ITO’s MIR\ntransmittance and potential thermal effects on device performance. We have addressed\nboth aspects through ITO MIR transmittance and comprehensive thermal investigation.\n(1) To characterize the optical properties of our ITO films in the MIR range, we\nperformed simulations based on the Drude model (Journal of Applied Physics, 1986,\n60(11), R123-R160.; Journal of Applied Physics, 1999, 86(11), 6451-6461.). For\nconductive oxides, the complex permittivity can be expressed as ε(ω) = ε - ω ²/(ω² +\n∞ p\niωΓ), where ε is the high-frequency permittivity, ω is the plasma frequency\n∞ p\ndetermined by carrier concentration, and Γ is the damping coefficient. For our ITO\nfilms with sheet resistance of 100 Ω/sq and thickness of 50 nm, the calculated carrier\nconcentration is ~ 3×1020 cm-3. As shown in Figure R14, these results reveal that these\nITO film maintains a transmittance ~0.7 at 10.6 μm wavelength, demonstrating\nreasonable transparency in the MIR range.\n22\n(2) To comprehensively address the reviewer’s concern about thermal effects, we\nconducted a detailed thermal characterization of the complete device.\nFirst, we quantified the magnitude and impact of MIR-induced heating. Using\ninfrared thermal imaging under MIR illumination (56.6 mW cm-2, 2s), we observed a\ntemperature rise of 2.8 K with corresponding photocurrent of 4.9 nA. To distinguish\nthe resulting thermal contribution to the photocurrent, we performed controlled heating\nexperiments. We determined that this 2.8 K temperature increase induces a current\nchange of ~1.2 nA. This suggests that the photothermal effect arises from conductivity\nmodifications in entire device components (ITO, MoO /CNT, substrates etc.), accounts\n3\nfor ~24.5% of total photocurrent.\nFurthermore, to directly investigate the influence on contact properties, we\nmeasured the I-V curves of the device both at room temperature and under heated\nconditions (Figure S18b). The device exhibited consistent ohmic behavior in both\nstates, indicating that the MIR-induced heating has not significantly alter the nature of\nthe electrical contact between the ITO electrode and the active layer.\nRevision.\nFigure R14 (Figure S18 in revised manuscript). (a) MIR transmittance spectrum of\n50 nm ITO film (Rsquare = 100 Ω/sq). (b-c) Device I-V curves without heating (26°C,\nb) and with heating (28.8°C, c). Both conditions exhibit ohmic contact characteristics.\nevolution during 10 consecutive MIR pulses. The photocurrent shows an accumulative\nincrease with pulse number while the device temperature remains stable throughout the\nmeasurement.\nRevision.\nNote S7. Investigation of thermal effects contributions in MIR photosynapse\nbehaviors.\nInfrared thermal imaging showed that MIR illumination (10.6 μm, 56.6 mW cm-2,\n2s duration) induces a total temperature rise of 2.8 K across entire device components\n(Figure S19), corresponding to a photocurrent of 4.9 nA. Control experiments using a\nthermal stage revealed that this 2.8 K increase generates ~1.2 nA of current (Figure\nS20). To further investigate the origin of synaptic behavior, we applied pulsed MIR\nillumination while monitoring both photocurrent and temperature. The photocurrent\n17\nexhibited characteristic accumulative enhancement over 10 pulses (from 4.9 nA for the\nfirst pulse to 7.5 nA for the tenth pulse, Figure S21), while the device temperature\nremained stable throughout (Figure S21). This decoupling between accumulating\nphotocurrent and stable temperature supports that synaptic plasticity is predominantly\ngoverned by photoelectric processes. Furthermore, I-V measurements confirmed that\nthe ohmic characteristics are maintained within the temperature range (Figure S18).\n24\n7. Polydopamine (PDA) is used as a surface modifier to improve film uniformity.\nThe authors should clarify whether PDA exhibits any optical absorption in the UV-to-\nMIR range or contributes defect states at the CNT/MoO3 or interface that may\naffect the photosynaptic behavior.\nResponse: We thank the reviewer for this important question regarding the\npotential influence of polydopamine (PDA) on the photosynapse behavior. We have\nperformed further comprehensive characterization to investigate the optical absorption\nand influence of defect states.\nOptical characterization reveals that PDA exhibits broad absorption across the\nUV-Vis, NIR, and MIR spectral ranges (Figure R16). Despite this wide absorption, the\ncontribution of PDA to the photosynaptic behaviors remains limited. In the UV-Vis-NIR region, although PDA absorbs photons, the photogenerated carriers suffer from\nlimited charge transport in the amorphous polymer, preventing effective injection into\nthe CNT/MoO heterojunction (Langmuir 2013, 29, 8619–8628; BioNanoSci. 2012, 2,\n3\n16–34). In the MIR region, photon absorption induces vibrational excitations rather\nthan electronic transitions, inherently precluding effective photocarrier generation.\nTemperature measurements under MIR illumination reveal that temperatures remain\nstable over consecutive pulses (Figure R15), indicating no significant contribution to\nsynaptic behavior through thermal effects.\nTo further evaluate whether PDA affects the defect states of device, we performed\nXPS analysis on MoO /CNT films deposited with and without PDA interlayer\n3\nmodification (Figure R17). Both samples were subjected to mild Ar+ etching to access\nthe buried MoO layer while maintaining consistent measurement conditions. The Mo\n3\n3d spectra revealed nearly identical Mo5+/Mo6+ ratios: 1:2.03 for the PDA-modified\nsample and 1:2.05 for the control sample. These results indicate no significant influence\nof the PDA modification on the MoO defect states.\nsignificantly altering the photoelectronic properties of the active layers. Detailed\nspectroscopic analysis and mechanistic discussion are provided in Note S2.\nfilm across different spectral regions. (a) UV-Vis spectrum showing broad absorption\nband centered around 600 nm. (b) NIR spectrum exhibiting continuous absorption from\n1000 to 2000 nm. (c) MIR spectrum displaying characteristic vibrational peaks\ncorresponding to various molecular vibrations of the PDA structure.\nFigure R17 (Figure S8 in revised manuscript). XPS Mo 3d spectra of MoO films.\n3\n(a) MoO /CNT film without PDA interface showing Mo5+/Mo6+ ratio of 1:2.05. (b)\n3\nexhibit similar Mo5+/Mo6+ ratios and peak positions.\nRevision.\nOptical characterization shows PDA absorption spanning UV to MIR wavelengths.\nHowever, in the UV-Vis-NIR regions, photogenerated carriers suffer from poor\ntransport in the amorphous polymer45,46, while MIR absorption induces molecular\nvibrations without carrier generation, indicating limited contribution to the\n26\nphotoelectric response. XPS analysis of Mo 3d spectra shows nearly identical\nMo5+/Mo6+ ratios (1:2.03 vs 1:2.05) with and without PDA modification. These results\nindicate that PDA functions primarily as a surface modifier without significantly\naltering the photoelectronic properties of the active layers (see detailed analysis in Note\nS2 and Figure S7-8).\nReferences.\n45. Zangmeister, R. A., Morris, T. A. & Tarlov, M. J. Characterization of\nPolydopamine Thin Films Deposited at Short Times by Autoxidation of Dopamine.\nLangmuir 29, 8619–8628 (2013).\n46. Ball, V. et al. Deposition Mechanism and Properties of Thin Polydopamine\nFilms for High Added Value Applications in Surface Science at the Nanoscale.\nBioNanoScience 2, 16–34 (2012).\nNote S2. Analysis of PDA contribution for photosynapse behaviors.\nTo evaluate whether the PDA interlayer contributes to photosynaptic behavior\nthrough optical absorption or defect state modification, we performed comprehensive\ncharacterization. Optical spectroscopy revealed that PDA exhibits broad absorption\nacross the entire UV-MIR spectrum (Figure S7). In the UV-Vis region (Figure 7a),\nPDA exhibits broad absorption centered around 600 nm, attributed to charge transfer\nbetween catechol-quinone moieties and π-π* transitions. The NIR region (Figure 7b)\nshows continuous absorption due to extended conjugation structure. In the MIR region\n(Figure S7c), characteristic peaks appear including aromatic C=C stretching, N-H\nbending, and aromatic C-H bending17.\nDespite the wide absorption from UV to MIR, the contribution of PDA to the\nphotosynaptic behaviors remains limited. In the UV-Vis-NIR region, although PDA\nabsorbs photons, the photogenerated carriers suffer from limited charge transport in the\namorphous polymer, preventing effective injection into the CNT/MoO\n3\nheterojunction18,19. In the MIR region, photon absorption induces vibrational\nexcitations rather than electronic transitions, inherently precluding photocarrier\ngeneration. Thermal measurements of PDA-modified devices reveal a temperature\n27\nincrease of ~2.8°C under MIR illumination without obvious accumulation over\nconsecutive light pulses, suggesting limited contribution on the synaptic behavior\nthrough thermal effects.\nTo further evaluate whether PDA affects the defect states of device, we performed\nXPS analysis on MoO /CNT films deposited with and without PDA interlayer\n3\nmodification. Both samples were subjected to mild Ar+ etching to access the buried\nMoO layer while maintaining consistent measurement conditions. The Mo 3d spectra\n3\nrevealed nearly identical Mo5+/Mo6+ ratios: 1:2.03 for the PDA-modified sample and\n1:2.05 for the control sample. These results indicate no significant influence of the PDA\nmodification on the MoO defect states.\n17. Liu, Y., Ai, K. & Lu, L. Polydopamine and Its Derivative Materials: Synthesis\nand Promising Applications in Energy, Environmental, and Biomedical Fields. Chem.\nRev. 114, 5057–5115 (2014).\n18. Zangmeister, R. A., Morris, T. A. & Tarlov, M. J. Characterization of\nPolydopamine Thin Films Deposited at Short Times by Autoxidation of Dopamine.\nLangmuir 29, 8619–8628 (2013).\n19. Ball, V. et al. Deposition Mechanism and Properties of Thin Polydopamine\nFilms for High Added Value Applications in Surface Science at the Nanoscale.\nBioNanoSci. 2, 16–34 (2012).\n8. Figure 1a schematically illustrates the band alignment between CNT and MoO\n3\nbut does not provide direct experimental or theoretical support. The authors should\nindicate how this alignment was determined.\nResponse: We thank the reviewer for this important question. The band alignment\nof CNT and MoO shown in Figure 1a was determined experimentally through\n3\nultraviolet photoelectron spectroscopy (UPS) measurements combined with optical\nbandgap characterization. The work functions (Φ) were calculated from the secondary\nelectron cutoff energies (E ) using the relation Φ = hν - (E - E ), where hν =\ncutoff cutoff F\n28\n21.22 eV for He I excitation source and E is the Fermi level. From the UPS spectra\nF\nshown in Figure R18, we obtained E values of 17.30 eV for CNT and 15.06 eV for\ncutoff\nMoO , yielding work functions of 3.92 eV and 6.16 eV, respectively. The valence band\n3\nmaximum (VBM) positions relative to the Fermi level were determined from the onset\nof the valence band spectra (Figure R18), giving E values of 0.07 eV for CNT and\nonset\n2.26 eV for MoO . Therefore, the VBM positions relative to vacuum level are 3.99 eV\n3\n(3.92 + 0.07) for CNT and 8.42 eV (6.16 + 2.26) for MoO . The optical bandgaps\n3\ndetermined from Tauc plots (Figure 3j-k in original manuscript) are 3.3 eV for MoO\n3\nand 0.094 eV for CNT, allowing us to calculate the conduction band minimum positions\nat 5.12 eV for MoO and 3.90 eV for CNT relative to vacuum level. These experimental\n3\nvalues establish the complete band alignment diagram shown in Figure R19.\nFigure R18 (Figure S12 in revised manuscript). UPS spectra for band alignment\ndetermination. (a) Secondary electron cutoff region showing E values of 17.30 eV\ncutoff\nfor CNT and 15.06 eV for MoO . (b) Valence band region showing onset energies of\n3\n0.07 eV for CNT and 2.26 eV for MoO . (c) Energy band diagram of CNT and MoO .\n3 3\n(CBM), valence band maximum (VBM), and Fermi level (E ) positions are indicated\nF\nrelative to the vacuum energy level.\nRevision:\nThe work function difference between MoO (6.16 eV) and CNT (3.92 eV) creates\n3\nan asymmetric interfacial energy landscape (see more detailed band alignment\nmeasurement in Note S5) that drives photoexcited electrons from CNT to MoO upon\n3\nillumination.\nNote S5. Band structure measurement of CNT and MoO .\nenergies (E ) using the relation Φ = hν - (E - E ), where hν = 21.22 eV for He I\ncutoff cutoff F\nexcitation source and EF is the Fermi level. From the UPS spectra shown in Figure\nS12, we obtained Ecutoff values of 17.30 eV for CNT and 15.06 eV for MoO , yielding\n3\nwork functions of 3.92 eV and 6.16 eV, respectively. The valence band maximum\n(VBM) positions relative to the Fermi level were determined from the onset of the\nvalence band spectra (Figure S12), giving E values of 0.07 eV for CNT and 2.26\nonset\neV for MoO . Therefore, the VBM positions relative to vacuum level are 3.99 eV (3.92\n3\n+ 0.07) for CNT and 8.42 eV (6.16 + 2.26) for MoO . The optical bandgaps determined\n3\nfrom Tauc plots (Figure 3j-k) are 3.3 eV for MoO and 0.094 eV for CNT, allowing\n3\nus to calculate the conduction band minimum positions at 5.12 eV for MoO and 3.90\n3\neV for CNT relative to vacuum level. These experimental values establish the complete\nband alignment diagram shown in Figure S13.\n30\n9. The authors show double-exponential decay times (Figure 3l) that increase with\nwavelength. In particular, MIR responses exhibit much slower decay than UV–Vis. Is\nthis due to different carrier dynamics or trap distributions? A more detailed\ndiscussion of wavelength-dependent dynamics is warranted, especially for synaptic applications\nrequiring high-speed response.\nResponse: We thank the reviewer for important suggestion. The wavelength-\ndependent decay differences can be attributed to the influence of carrier generation\nmechanisms and trap states filling levels. Higher-energy UV and Vis photons generate\ncarriers in both CNTs and MoO , leading to efficient trap filling and faster trap-assisted\n3\nrecombination with relatively short decay time constants (Nature Communications\n2021,12.1, 3543.; Appl. Surf. Sci. 2023, 633, 157594). In contrast, MIR photons (10.6\nμm) generate relatively low-density carriers only through narrow-bandgap CNT\nabsorption (Applied Physics Letters 2004, 85.19, 4463-4465.), resulting in partial trap\nfilling and slower detrapping/recombination processes with extended decay times (Adv.\nFunct. Mater. 2020, 30.33, 2002342.). Additionally, MIR illumination induces a local\ntemperature rise, and the thermal relaxation after illumination cessation may further\ncontribute to the extended decay (Adv. Mater. 2019, 31, 1902044).\nRegarding high-speed applications, there is an inherent trade-off between synaptic\nmemory retention and response speed. Memory retention in synaptic devices requires\nmaintaining conductance states after stimulus removal for temporal information\nintegration, while rapid response demands fast recovery to the baseline state for\nprocessing new inputs. This trade-off implies that synaptic applications with different\noperational requirements need appropriately matched decay characteristics. This\nphotosynapse device with moderate time constants is suitable for applications without\nstringent speed requirements while benefiting from memory effects.\nWe have incorporated this discussion into the main text.\nRevision.\nThe wavelength-dependent decay differences can be attributed to the influence of\ncarrier generation mechanisms and trap states filling levels. Higher-energy UV and Vis\n31\nphotons generate carriers in both CNTs and MoO , leading to efficient trap filling and\n3\nfaster trap-assisted recombination with relatively short decay time constants 63,64. In\ncontrast, MIR photons (10.6 μm) generate relatively low-density carriers only through\nnarrow-bandgap CNT absorption, resulting in partial trap filling and slower detrapping\nas well as recombination processes with extended decay times.65 Additionally, MIR\nillumination induces a local temperature rise (Figure S19), and the thermal relaxation\nafter illumination cessation may further contribute to the extended decay66. This\nphotosynapse device with moderate time constants is suitable for applications without\nstringent speed requirements while benefiting from memory effects.\nReferences.\n63. Leijtens, T. et al. Carrier trapping and recombination: the role of defect physics\nin enhancing the open circuit voltage of metal halide perovskite solar cells. Energy\nEnviron. Sci. 9, 3472–3481 (2016).\n64. Zeiske, S. et al. Direct observation of trap-assisted recombination in organic\nphotovoltaic devices. Nat. Commun. 12, 3603 (2021).\n65. Trimpl, M. J. et al. Charge‐Carrier Trapping and Radiative Recombination in\nMetal Halide Perovskite Semiconductors. Adv. Funct. Mater. 30, 2004312 (2020).\n66. Lu, X., Sun, L., Jiang, P. & Bao, X. Progress of Photodetectors Based on the\nPhotothermoelectric Effect. Adv. Mater. 31, 1902044 (2019).\n10. The manuscript lacks light-intensity-dependent responsivity data. This\ninformation is critical to evaluate linearity, dynamic range, and potential gain effects\nunder different optical power levels across spectral bands. Such measurements would\nalso help distinguish synaptic accumulation from trap saturation effects.\nResponse: We thank the reviewer for the valuable suggestion. Following the\nreviewer’s advice, we have performed a systematic study of the photoresponse as a\nfunction of incident light intensity across different spectral bands (Figure R20 and\nR21). These results have been added to the revised manuscript (Figure S15 and 16).\nph\nwas analyzed. It is found that the photoresponse follows a sub-linear power-law\ndependence, described by the equation I ∝ Pα. Across the tested 365 nm, 532 nm,\nph\n1064 nm, and 10.6 μm wavelengths, the exponent α was consistently determined to be\nin the range of 0.81 to 0.85. The calculated LDR was found to be 17.8 dB (14.15 to 849\nmW cm-2) for the 10.6 μm and 22.6 dB (1 ~180 mW cm-2) for the 365 nm, 532 nm,\n1064 nm region，with no significant gain effect observed throughout these intensity\nranges.\nThese power-dependent photoresponse measurements allow us to distinguish the\ngradual accumulation process from the saturation regime. An exponent α < 1 is\ncharacteristic of photodetectors where trap states play a significant role in the\nphotoconduction process (Nature Nanotechnology 2013, 8, 497–501), which is\nconsistent with the proposed accumulation in our CNT/MoO system. At higher light\n3\nintensities, we observed a deviation from this power law, which indicates the onset of\ntrap state saturation (Nature Nanotechnology 2012, 7, 363–368).\ncharacteristics of the photosynapse device. Responsivities were measured at\nwavelengths of (a) 365 nm, (b) 532 nm, (c) 1064 nm, and (d) 10.6 μm under various\noptical power densities. Test conditions: 2 s illumination duration, 0.5 V bias, 26°C.\nFigure R21 (Figure S16 in revised manuscript). Dual-log plots of photocurrent\nversus optical intensity at (a) 365 nm, (b) 532 nm, (c) 1064 nm, and (d) 10.6 μm. Power-\nlaw fits yield exponents of 0.82, 0.81, 0.83, and 0.85, respectively, demonstrating\nsublinear photoresponse at these wavelengths.\nRevision.\nTo evaluate the linearity and dynamic range of our photosynapse devices, we\ncharacterized the intensity-dependent photoresponse across the wide spectral range\n(Figure S15 and 16). Detailed wavelength-dependent responsivity measurements\n34\ndemonstrate effective wide-spectrum photoresponse from UV to MIR range (Figure\nS15). The photocurrent exhibits a sublinear power-law dependence on incident optical\npower (I ∝ Pα) with α < 1 across all tested wavelengths from UV to MIR, suggesting\nph\ntrap-related photoresponse processes (see Note S6 for detailed analysis).\nNote S6 Intensity-dependent photoresponse analysis.\nAs shown in Figure S15 and S16, the exponent α was consistently determined to\nbe in the range of 0.81 to 0.85 across the tested 365 nm, 532 nm, 1064 nm, and 10.6\nμm wavelengths. The calculated LDR was found to be 17.8 dB (14.15 to 849 mW cm-\n2) for the 10.6 μm and 22.6 dB (1 ~180 mW cm-2) for the 365 nm, 532 nm, 1064 nm\nregion，with no significant gain effect observed throughout these intensity ranges.\nThese power-dependent photoresponse measurements allow us to distinguish the\ngradual accumulation process from the saturation regime. An exponent α < 1 is\ncharacteristic of photodetectors where trap states play a significant role in the\nphotoconduction process21, which is consistent with the proposed accumulation in our\nCNT/MoO system. At higher light intensities, we observed a deviation from this power\n3\nlaw, which indicates the onset of trap state saturation22.\nReferences.\n21. Lopez-Sanchez, O., Lembke, D., Kayci, M., Radenovic, A. & Kis, A.\nUltrasensitive photodetectors based on monolayer MoS2. Nature Nanotech 8, 497–501\n(2013).\n22. Konstantatos, G. et al. Hybrid graphene–quantum dot phototransistors with\nultrahigh gain. Nature Nanotech 7, 363–368 (2012).\n11. Given that thermal radiation from real-world targets at room temperature peaks\nnear 10 μm, the authors should discuss whether their array can effectively image\ndynamic thermal scenes without external illumination. Demonstrating passive MIR\ndetection at room temperature would significantly strengthen the practical impact of the\nwork.\nimaging capabilities. We have attempted to image room-temperature thermal targets\n(human hand, ~310 K) under ambient conditions without external illumination.\nUnfortunately, we were unable to observe clear images with our current photosynapse\narray. We attribute this limitation to two main factors. First, the thermal radiation from\nroom-temperature objects is inherently weak and diffuse (Introduction to Radiometry\nand Photometry by W.R. McCluney, 2nd ed.,2014), in contrast to the focused laser\nillumination used in active imaging experiments. Second, detecting such weak thermal\nsignals requires ultra-low noise levels. The residual metallic CNTs in solution-\nprocessed films can increase dark current (ACS Nano 2014, 8, 3434; Nano Lett. 2006,\n6, 1564), while amorphous silicon readout TFTs exhibit inherent 1/f noise due to charge\ntrapping process (IEEE Trans. Electron Devices 1989, 36, 2753). These noise sources,\nthough acceptable for active imaging applications with large light intensity, present\nchallenges for detecting the ultra-weak signals in passive thermal imaging.\nTo achieve passive thermal imaging in future work, potential strategies include\nimproving the semiconducting CNT purity through advanced sorting techniques to\neffectively reduce the noise level (Science,2020, 368(6493), 850-856.). Additionally,\noptimizing the device architecture and integrating low-noise readout circuits could\nfurther reduce the noise level (IEEE Trans. Electron Devices, 2004, 51, 185-194.).\nThese improvements could potentially enable the detection of thermal radiation from\nroom-temperature objects. We have incorporated this discussion into the revised\nmanuscript.\nRevision.\nPassive thermal imaging of room-temperature objects without external\nillumination, however, remains challenging. This limitation may be attributed to the\nrelatively large noise from residual metallic CNTs and 1/f noise in amorphous silicon\nTFTs. Potential strategies include achieving higher semiconducting CNT purity\n36\nthrough advanced sorting techniques67, and integrating silicon CMOS readout circuits68\nfor improved noise performance.\nReferences.\n67. Liu, L. et al. Aligned, high-density semiconducting carbon nanotube arrays for\nhigh-performance electronics. Science 368, 850–856 (2020).\n68. Kawai, N. & Kawahito, S. Noise Analysis of High-Gain, Low-Noise Column\nReadout Circuits for CMOS Image Sensors. IEEE Trans. Electron Devices 51, 185–\n194 (2004).\nThe authors present a 64k-scale photosynapse array based on CNT/MoO3\nheterojunctions for neuromorphic vision applications. The device demonstrates ultra-\nwide spectral response from UV to MIR and achieves high fabrication yield through\npolydopamine surface modification. The demonstrated improvements in trajectory\nprediction and recognition accuracy under challenging conditions are encouraging. The\nmanuscript is generally well-organized and the experimental results are comprehensive.\nI recommend this work for publication in Nature Communications with minor revisions\nas detailed below:\nResponse: We sincerely thank the reviewer for the encouraging evaluation and\nconstructive suggestions. We have carefully addressed all the raised points through\nadditional experiments and analyses, which have strengthened the manuscript. Our\ndetailed point-by-point responses are provided below.\n(1) The authors demonstrate that polydopamine treatment improves device\nuniformity. To further strengthen the discussion on surface modification stability, it is\nrecommended that the authors supplement a water contact angle measurement after\nstorage (e.g., after 30 days in ambient conditions).\nResponse: We thank the reviewer for this valuable suggestion. We have\nperformed water contact angle measurements after 30 days of ambient storage to\nevaluate the long-term stability of PDA surface modification. As shown in Figure R22,\nthe results show that the contact angle variation is ~ 1.1 % (from 27.4° to 27.7°),\nsuggesting the stability of the PDA treatment. This discussion has been incorporated\ninto the revised manuscript.\nPDA-modified ITO surface before and after 30 days of ambient storage, demonstrating\nthe stability of surface modification.\nRevision.\nThe contact angle remained stable after 30 days of ambient storage (Figure S6),\nillustrating the good stability of PDA modification.\n(2) The authors state that the Moran’s I index increases from 0.3010 (4k array) to\n0.7613 (64k array), indicating enhanced spatial clustering in larger arrays. It is\nsuggested the authors add a brief discussion on potential compensation strategies for\naddressing this issue, such as non-uniformity calibration algorithms.\nResponse: We appreciate the reviewer’s valuable suggestion regarding\ncompensation strategies. The increasing Moran's I index from 0.3010 to 0.7613 indeed\nindicates spatial correlations in dark current distribution, which can be primarily\nattributed to fixed-pattern noise from fabrication-induced edge effects. These edge\neffects, caused by topographical variations where interconnects route to peripheral pads,\nbecome more pronounced in larger arrays. Future optimization strategies could include\nenhanced layout margins to minimize edge effects and implementation of non-\nuniformity correction algorithms (IEEE Trans. Image Process. 1999, 8, 1148–1151).\nWe have added this discussion to the revised manuscript.\nRevision.\nThe Moran’s I indices of 0.3010 (64×64 array) and 0.7613 (256×256 array)\nmeasured under dark conditions indicate spatial correlations in dark current distribution,\nwhich can be primarily attributed to fixed-pattern noise from fabrication-induced edge\neffects (see Note S3 for detailed analysis). These edge effects caused by topographical\nvariations where interconnects route to peripheral pads, become more pronounced in\nlarger arrays, as supported by the spatial clustering of failed pixels in edge regions\n(Figure S10). Future potential improvements include optimized peripheral routing\ntopology to minimize edge effect, combined with adaptive non-uniformity correction\n(NUC) algorithms employing two-point calibration or scene-based statistical methods\nto compensate for residual fixed-pattern noise 47,48.\nReferences.\n47. Harris, J. G. & Yu-Ming Chiang. Nonuniformity correction of infrared image\nsequences using the constant-statistics constraint. IEEE Trans. Image Process. 8, 1148–\n1151 (1999).\n48. Milton, A. F., Barone, F. R. & Kruer, M. R. Influence Of Nonuniformity On\nInfrared Focal Plane Array Performance. Opt. Eng. 24, (1985).\n(3) The authors mention using a “queen contiguity configuration” for Moran’s I\ncalculation without explanation. Please provide a brief clarification of this spatial\nweight matrix configuration for readers unfamiliar with spatial statistics terminology.\nResponse: We thank the reviewer for pointing out this omission. We have now\nadded a clarification of the queen contiguity configuration to the revised manuscript.\nWe used a queen contiguity configuration where pixels sharing edges or corners are\nconsidered adjacent. The spatial weight wᵢⱼ was defined using a queen contiguity\nconfiguration where pixels sharing edges or corners were assigned a value of 1 and 0\notherwise, with W representing the sum of all weights.\nThis clarification has been added to the revised manuscript.\nRevision.\nWe used a queen contiguity configuration where pixels sharing edges or corners\nare considered adjacent. The spatial weight wᵢⱼ was defined using a queen contiguity\nconfiguration where pixels sharing edges or corners were assigned a value of 1 and 0\notherwise, with W representing the sum of all weights.\n(4) The authors report achieving 99.96% pixel yield in the 64k array. It would be\nhelpful if the authors could provide a spatial map or brief description of the non-\n40\nfunctional pixel distribution to understand whether defects are randomly distributed or\nshow clustering patterns.\nResponse: Thank you for your valuable suggestion. We have conducted a detailed\nstatistical analysis of the spatial distribution of the 23 non-functional pixels, and the\nresults are presented in Figure R23. The analysis reveals a clear spatial clustering\npattern: all non-functional pixels are concentrated in the corner regions of the array.\nThis edge-concentrated failure mode is consistent with edge effects commonly\nobserved in large-scale array fabrication (ACS Applied Materials & Interfaces, 2022,\n14(37): 42365-42373.; Acta Materialia, 2001, 49(1): 93-107.). In our case, the\ninsufficient spacing between electrode pads and the active array allows the\nphotoresist edge effects to extend into the active region. The step discontinuities formed\nduring pad protection affect PDA deposition uniformity at array corners (Journal of\nApplied Physics, 1998, 84(7): 3636-3649.). Potential improvements could include\nimplementing dedicated corner structures, extending the guard ring design into corner\nregions, or incorporating dummy pixels at the array periphery to shield active pixels\nfrom edge-related variations.\nWe have incorporated this spatial distribution analysis (Figure S10) and\ndiscussion into the revised manuscript.\nFigure R23 (Figure S10 in revised manuscript). (a) Spatial distribution of effective\nphotocurrent (I =I -I ) values. (b) Enlarged view of locations for non-\neff photo photo dark\nfunctional pixels.\nRevision.\nNote S3. Analysis of spatial distribution of non-functional pixels.\npixels are concentrated in the array corners. This edge-concentrated failure mode may\nbe attributed to insufficient spacing between electrode pads and the active array, which\nallows photoresist edge effects to extend into the active region. The step discontinuities\nformed during pad protection specifically affect PDA deposition uniformity at array\ncorners. Potential improvements could include implementing dedicated corner\nstructures, extending the guard ring design into corner regions, or incorporating dummy\npixels at the array periphery to shield active pixels from edge-related variations.\n(5) In Figure 4 caption, there appears to be a typographical error. “365 nm visible\nlight” and “532 nm UV light” should be revised.\nResponse: We appreciate the reviewer for identifying this error. Indeed, 365 nm\nfalls within the UV range while 532 nm is in the visible spectrum (green light). We have\ncorrected this typographical error in the Figure 4 caption, which now accurately\ndescribes the wavelengths as \"365 nm UV light\" and \"532 nm visible light\" in the\nrevised manuscript.\nRevision.\nCaption of Figure 4: (b) Transparent borosilicate glass container with metallic ring:\n(i) photograph, (ii) 365 nm UV light response image, and (iii) 532 nm Vis light response\nimage showing consistent visibility across both wavelengths.\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers who\nco-review manuscripts.\nResponse: We thank the co-reviewer for their time and effort in reviewing our\nmanuscript.\nThe authors present a wide-spectrum photosynapse array based on CNT/MoO3\nheterojunction with the largest demonstrated array size and response spectrum. A\ncompelling case for wide response spectrum is made with side-by-side comparison of\na glass bottle containing a metal ring that is imaged using UV light and visible light.\nBoth imaging results are shown to contain important semantic information (either\nexterior morphology or metallic ring). The presented photosynapse array shows strong\npotential for realizing a wide-spectrum combined sensing + processing system applied\nto learning/robotics.\nThe following comments and questions are provided with regard to this work:\nResponse: We sincerely appreciate the reviewer’s encouraging comments on our\nwork on the wide-spectrum photosynapse array based on CNT/MoO heterojunctions.\nadditional experiments, comparative discussions, and supplementary characterizations,\nas detailed in our point-by-point responses below.\n1. In Line 48-51, authors mention that visible spectrum + CMOS sensors perform\npoorly in adverse visibility conditions. Currently, dynamic vision sensors (DVS), which\ntransmit sparse, event-driven spikes, also offer heightened computer vision (CV)\nperformance in challenging conditions, like fog and smoke, due to higher temporal\nresolution, reduced motion blur, and logarithmic light sensitivity (and therefore high\ndynamic range) when compared to RGB cameras.\nSee https://arxiv.org/pdf/1904.08405 and https://arxiv.org/pdf/2404.10540 for more\ndetails. Additionally, due to DVS emulating the retina, they are well-suited to integrate\ninto neuromorphic systems that aim to unite brain-like computation and brain-like\nsensory peripherals. Lastly, there is an established community of neuromorphic CV and\nrobotics engineers that have designed and optimized software packages, like LavaDL\n(for Loihi 2 simulation/programming) and Neuromorphic Intermediate Representation\n(connecting LavaDL, snnTorch, SpiNNaker2, Norse, etc.), for a streamlined DVS to\ncomputational hardware to learning/robotics pipeline. How does the proposed\n44\nphotosynapse array compare with DVS/event cameras? Specifically, what tasks would\nthe proposed system outperform an RGB + DVS system and what is the\nfrequency/criticality of such tasks (e.g., in the context of autonomous vehicles)?\nResponse: Thank you for this important comparison with Dynamic Vision\nSensors (DVS). We acknowledge that DVS technology represents a significant\nadvancement in neuromorphic vision, and appreciate the opportunity to clarify the\ndistinct contribution of our photosynapse array.\nOur photosynapse array addresses scenarios where spectral coverage beyond the\nvisible range becomes critical for effective perception. While DVS systems excel in\nvisible-spectrum applications with microsecond temporal resolution and established\nneuromorphic ecosystems (LavaDL, NIR), our system extends neuromorphic sensing\ninto UV and MIR regions (365 nm to 10.6 μm) through single-device integration of\nmulti-spectral detection with neuromorphic processing, enabling perception beyond\nvisible limitations.\nOur system has potential to provide unique capabilities in two critical scenarios:\nFirst, thermal object detection in complete darkness, where stationary warm objects,\npedestrians, or animals can be detected through their 8-12 μm thermal signatures when\nboth RGB and DVS systems fail due to absence of visible light. (IEEE Trans. on\nIntelligent Transportation Systems, 2005, 6(1), 63-71.) Second, operation under\nadverse weather conditions, where MIR sensing maintains functionality in fog, smoke,\nand dust due to superior transmission of MIR wavelengths through atmospheric\nobscurants compared to visible wavelengths (International Journal of Computer\nVision, 2002, 48, 3, 233-254). While these extreme scenarios (nighttime/low-light\ncondition, severe weather) occur with limited frequency, they represent precisely the\nhigh-risk situations that lead to fatal accidents. Reliable perception in these edge cases\nis critically important for safe autonomous driving (Accident Analysis & Prevention,\n2013, 53, 65-77.; Accident Analysis & Prevention, 2011, 43(5), 1730-1737.;\nTransportation Research Record, 2008, 2055(1), 139-146.)\nRGB+DVS+Photosynapse configurations, where DVS handles high-speed visible\nevents while our system provides crucial UV/MIR semantic information, particularly\nwhen visible perception is compromised. This multi-modal fusion could significantly\nenhance system robustness and all-weather operation capability. Our technology is\npositioned as an important complement to existing DVS systems, together building a\nmore comprehensive neuromorphic perception framework.\nRevision.\nIntroduction.\nCurrent automotive sensing relies predominantly on visible (Vis) spectrum\ncomplementary metal oxide semiconductor (CMOS) sensors. For example, dynamic\nvision sensors (DVS) have demonstrated impressive performance in challenging\nconditions5,6. However, these sensors remain limited to the visible spectrum, whereas\nmany critical autonomous navigation scenarios require perception beyond visible\nwavelengths7–9.\nReferences.\n5. Aliminati, M. R. et al. SEVD: Synthetic Event-based Vision Dataset for Ego\nand Fixed Traffic Perception. Preprint at https://doi.org/10.48550/arXiv.2404.10540\n(2024).\n6. Gallego, G. et al. Event-Based Vision: A Survey. IEEE Trans. Pattern Anal.\nMach. Intell. 44, 154–180 (2022).\n7. Bullough, J. D., Donnell, E. T. & Rea, M. S. To illuminate or not to illuminate:\nRoadway lighting as it affects traffic safety at intersections. Accid. Anal. Prev. 53, 65–\n77 (2013).\n8. Abdel-Aty, M., Ekram, A.-A., Huang, H. & Choi, K. A study on crashes\nrelated to visibility obstruction due to fog and smoke. Accid. Anal. Prev. 43, 1730–1737\n(2011).\n46\n9. Qiu, L. & Nixon, W. A. Effects of Adverse Weather on Traffic Crashes:\nSystematic Review and Meta-Analysis. Transp. Res. Rec. J. Transp. Res. Board 2055,\n139–146 (2008).\nResults.\nThis demonstrates that the wide-spectrum photosynapse array can complement\nvisible DVS systems by maintaining robust classification in challenging conditions,\nsuggesting potential for combined systems that utilize both technologies’ strengths in\nfuture applications.\n2. I can see how the proposed photosynapse array has strong potential to simplify\nexisting sensing/computing systems. However, I would like to see a clearer connection\nmade here. Namely, existing autonomous systems use a suite of sensors, including\nLiDAR, radar, RGB camera, IR/thermal. What portion of this sensor suite would the\nproposed system simplify?\nResponse: Thank you for this insightful question about the practical integration\nof our photosynapse array into existing autonomous sensing systems. We appreciate\nthe opportunity to clarify how our technology could simplify current sensor suites.\nOur photosynapse array could potentially simplify the imaging portion of\nautonomous sensor suites by integrating visible and thermal sensing capabilities, while\nLiDAR and radar would remain essential for depth and velocity measurements\nrespectively. This integration could reduce the imaging sensor count from 2-3 separate\nunits (RGB and thermal/IR cameras) to a single integrated device.\nThe potential simplification arises from our photosynapse array’ demonstrated\nbroadband photoresponse and in-situ processing capabilities. Based on our\nexperimental results showing sensitivity across visible and infrared wavelengths, this\napproach could potentially consolidate the functions of separate RGB and thermal\ncameras into a single integrated device, which may help reduce the number of imaging\nsensors and associated data fusion requirements (Sensors, 2021, 21(6), 2140.；\nFrontiers in Neuroscience, 2016, 10, 115.). While further development and\n47\noptimization would be needed, these initial results suggest a possible approach toward\nsimplifying the passive imaging components in autonomous vehicle sensor suites.\nWe have added the discussion to our revised manuscript:\nRevision.\nThis wide spectrum photoresponse spanning visible to 10.6 μm wavelengths\nsuggests that our photosynapse device could potentially simplify multiple imaging\nsensors such as RGB and IR camera in autonomous vehicles into a single device,\nreducing system complexity while maintaining multi-spectrum perception\ncapabilities.55,56\nReferences.\n55. Yeong, D. J., Velasco-Hernandez, G., Barry, J. & Walsh, J. Sensor and Sensor\nFusion Technology in Autonomous Vehicles: A Review. Sensors 21, 2140 (2021).\n56. Vanarse, A., Osseiran, A. & Rassau, A. A Review of Current Neuromorphic\nApproaches for Vision, Auditory, and Olfactory Sensors. Front. Neurosci. 10, (2016).\n3. In Line 191, a Moran’s I index of 0.3010 for dark current is mentioned. This suggests\nthere is some spatial correlations in the visual scene. Is this from some fixed-pattern\nnoise, thermal leakage, or electrical crosstalk between nearby devices? Additionally, in\nLine 197, was the higher Moran’s I index of 0.7613 also in dark conditions? Am I\ncorrect in my understanding that Moran’s I index should be close to zero in dark\nconditions? If not, could this be clarified more in the manuscript?\nResponse: Thank you for this important comment about the Moran’s I index\nvalues. We confirm that both Moran’s I values (0.3010 for the 64×64 array and 0.7613\nfor the 256×256 array) were measured under dark conditions. We agree that that ideally\nthe Moran’s I index should approach zero under dark conditions, indicating spatially\nuncorrelated random noise. The relatively large values we observed reveal spatial\ncorrelations in our dark current distribution that warrant explanation.\nThese spatial correlations can be mainly attributed to the fixed-pattern noise (FPN)\ninherent to our fabrication process. In our 1-transistor-1-sensor array design,\n48\ninterconnects are routed to edge pads, where photoresist protection creates\ntopographical variations. During PDA deposition, photoresist-protected areas introduce\nstep-height discontinuities that cause shadowing and non-uniform coverage. The higher\nMoran’s I value in the 256×256 array indicates that these edge effects become more\npronounced with increased array size. This is consistent with our observation that the\n23 failed pixels showed spatial clustering in edge regions rather than random\ndistribution (Figure R24). While thermal leakage and electrical crosstalk may also\ncontribute to the observed correlations, the 1-transistor-1-sensor architecture with\nindividual pixel selection helps suppress electrical crosstalk between neighboring\ndevices, and our temperature-controlled measurement conditions help minimize\nthermal leakage variations.\nThis analysis suggests future optimization through enhanced layout design with\nlarger pad-to-array margins, improved fabrication processes to minimize edge effects,\nand implementation of non-uniformity correction algorithms to compensate for residual\nspatial variations. We have added the detailed discussion of these findings in the revised\nmanuscript.\nFigure R24 (Figure S10 in revised manuscript). (a) Spatial distribution of effective\nphotocurrent (I =I -I ) values. (b) Enlarged view of locations for non-\neff photo photo dark\nfunctional pixels.\nRevision.\nThe Moran’s I indices of 0.3010 (64×64 array) and 0.7613 (256×256 array)\nmeasured under dark conditions indicate spatial correlations in dark current distribution,\nwhich can be primarily attributed to fixed-pattern noise from fabrication-induced edge\n49\neffects (see Note S3 for detailed analysis). These edge effects caused by topographical\nvariations where interconnects route to peripheral pads, become more pronounced in\nlarger arrays, as supported by the spatial clustering of failed pixels in edge regions\n(Figure S10). Future potential improvements include optimized peripheral routing\ntopology to minimize edge effect, combined with adaptive non-uniformity correction\n(NUC) algorithms employing two-point calibration or scene-based statistical methods\nto compensate for residual fixed-pattern noise 47,48.\nReferences.\n47. Harris, J. G. & Yu-Ming Chiang. Nonuniformity correction of infrared image\nsequences using the constant-statistics constraint. IEEE Trans. Image Process. 8, 1148–\n1151 (1999).\n48. Milton, A. F., Barone, F. R. & Kruer, M. R. Influence Of Nonuniformity On\nInfrared Focal Plane Array Performance. Opt. Eng. 24, (1985).\nNote S3. Analysis of spatial distribution of non-functional pixels.\nThe spatial distribution analysis (Figure S10) reveals that all 23 non-functional\npixels are concentrated in the array corners. This edge-concentrated failure mode may\nbe attributed to insufficient spacing between electrode pads and the active array, which\nallows photoresist edge effects to extend into the active region. The step discontinuities\nformed during pad protection specifically affect PDA deposition uniformity at array\ncorners. Potential improvements could include implementing dedicated corner\nstructures, extending the guard ring design into corner regions, or incorporating dummy\npixels at the array periphery to shield active pixels from edge-related variations.\n4. In Line 220-222, it is stated that the frequency-dependent photoresponse (i.e., the\nextent of synaptic relaxation/leakage) mimics the temporal filtering properties of\nbiological neural systems, allowing neural circuits to selectively respond to specific\ntemporal frequencies of input signals. It is very unclear how this is possible. My best\nguess is that synaptic decay/Laplace kernels act as low-pass filters (e.g., a Lorentzian-\n50\nstyle frequency response) which allow only certain frequencies to pass. But these are\nnot bandpass and thus would not respond to specific temporal frequencies with great\nprecision. Can this be explained more?\nResponse: We appreciate the reviewer’s insightful comment and valuable\nclarification. Through supplementary experiments comparing different frequencies,\nwe acknowledge that our device exhibits low-pass filtering characteristics with an\nexponential decay kernel, rather than bandpass selectivity. As shown in the Figure R25,\nhigher photocurrent responses at low frequencies (0.1 Hz) and decreased responses at\nhigher frequencies (0.5 Hz), demonstrating low-pass filtering with exponential decay\ndynamics. We have revised this discussion to address the reviewer’s concern.\nRevision.\nThis photoresponse mimics low-pass filtering properties characteristic of\nbiological synaptic dynamics, where the exponential decay creates a temporal\nintegration window that preferentially transmits low-frequency signals while\nattenuating high-frequency components52.\nFigure R25 (Figure 3b and S11 in revised manuscript). Frequency-dependent post-\nsynapse current response under 10.6 μm MIR stimulation. The device was subjected to\nthree consecutive pulse trains with different frequencies and corresponding ON/OFF\ntimes: 0.25 Hz (2 s ON/2 s OFF, Figure 3b), and 0.5 Hz (1 s ON/1 s OFF, Figure S11),\neach consisting of 10 pulses with 50% duty cycle. Experimental parameters: optical\npower density = 56.6 mW cm-2, device area = 0.04 mm2, bias voltage = 0.5 V,\ntemperature = 26°C.\n51\n5. Lines 281-288 mention the advantage of each frame encoding a time-weighted\nhistory of motion trajectories. This is standard in all DVS + neuromorphic vision\nsystems. See Fig. 2 in “HOTS: A Hierarchy of Event-Based Time-Surfaces for\nPattern Recognition” (https://ieeexplore.ieee.org/document/7508476) for an example. This is a\ndirect benefit of any system that appends an exponential decay kernel to visual events\nand is not a novel/unexpected result presented by this work. Learning systems that train\non DVS hardware data (with exponential decay kernel circuits or similar downstream\nprocessing in software) are highly competent at next-frame prediction of complex\ntrajectories, required, for example, in optical flow tasks.\nResponse: We thank the reviewer for this valuable context and the HOTS\nreference. We acknowledge that exponential decay kernels for temporal trajectory\nencoding are well-established in neuromorphic vision systems, and we appreciate this\nopportunity to better positioned and illustrated our work.\nAs the reviewer notes, temporal surface representations through exponential decay\nhave been successfully demonstrated in neuromorphic systems like HOTS for trajectory\nprediction and optical flow tasks. Our contribution focuses on implementing this\nestablished principle in a photosynapse array that operates across the UV-to-MIR\nspectrum. While conventional DVS systems perform temporal encoding in the visible\nrange, our device extends these neuromorphic computing capabilities to infrared and\nUV domains within a single hardware platform. This broadband implementation\nenables trajectory tracking from both thermal infrared and visible light sources, which\nis particularly relevant for various weather autonomous navigation.\nAdditionally, our photosynapse architecture provides direct analog output of the\naccumulated conductance state containing time-weighted trajectory information. DVS\nsystems with exponential decay kernel circuits have indeed demonstrated excellent\nperformance for trajectory prediction tasks. However, these systems typically still\nrequire reconstruction of temporal surfaces from event streams for many applications.\nOur approach maintains temporal information continuously in the analog domain\n52\nthrough intrinsic device physics, offering direct access to the time-weighted\nrepresentation without the intermediate step of temporal surface reconstruction. This\ncharacteristic may simplify the processing pipeline for certain real-time applications\nwhile preserving the temporal encoding benefits.\nWe have revised the manuscript to acknowledge the established nature of\nexponential-decay-based temporal encoding with appropriate citations, and reframed\nour discussion to position our contributions: the extension to UV-to-MIR spectrum and\nthe direct readout capability that may simplifies the processing pipeline.\nRevision:\nBeyond static imaging, the photosynapse array offers advantages in dynamic scene\nanalysis through its unique spatial-temporal response characteristics. Exponential\ndecay-based temporal encoding is well-established in neuromorphic vision systems69 .\nOur photosynapse implements this principle across the UV-to-MIR spectrum in\nhardware through stimulus-dependent conductance retention and gradual photocurrent\ndecay. This intrinsic integration function creates a system where current amplitude\ndirectly indicates stimulus recency, with recently activated pixels maintaining higher\nstates while earlier activated regions exhibit progressive attenuation. Consequently,\neach frame inherently encodes a time-weighted history of motion trajectories. The\nwide-spectrum photosynapse array have the potential for trajectory tracking both\nvisible and non-visible dynamic features. Moreover, our photosynapse architecture\noffers the potential to avoid temporal surface reconstruction, as the accumulated\nconductance state can be directly read out, simplifying prediction tasks that would\notherwise require reconstructing temporal surfaces from event streams.\nReferences.\n69. Lagorce, X., Orchard, G., Galluppi, F., Shi, B. E. & Benosman, R. B. HOTS:\nA Hierarchy of Event-Based Time-Surfaces for Pattern Recognition. IEEE Trans.\nPattern Anal. Mach. Intell. 39, 1346–1359 (2017).\n53\n6. For the last section, titled “Demonstration of neuromorphic fusion imaging,” it is\nlikely that a DVS-only or DVS + RGB input would yield a better result than the\nprovided conventional imaging hardware result. It would be a fairer comparison since\nDVS hardware data: (1) is better suited for hazy conditions and (2) can have built-in\nexponential decay kernel circuitry that preserves temporal history of events.\nFurthermore, different sensors require different learning models. So it would be more\nfair to use HOTS or EVFlowNet (https://arxiv.org/abs/1802.06898) -- with an\nequivalent number of parameters as the CNN used for the photosynapse array hardware\n-- for the DVS hardware data. In this way, the learning models would be of\ncomparable size/capacity, incurring comparable energy consumption, but would be tailored to their specific input data.\nResponse: Thank you for this suggestion regarding a more comprehensive\ncomparison with DVS-based approaches. We acknowledge that comparing with DVS\nhardware using specialized algorithms would provide valuable insights. Following your\nrecommendation, we implemented comprehensive comparisons using both HOTS and\nEVFlowNet frameworks. To ensure fair comparison as requested, we configured\nnetwork versions with comparable complexity to our 22.5 K-parameter photosynapse\narray: EVFlowNet (24.6 K parameters), HOTS (22.5 K parameters), fused DVS and\nRGB (25.6 K parameters) match our parameter count. This configuration ensures that\nthe learning models are of comparable size and capacity, incurring similar energy\nconsumption while being optimized for their respective input modalities.\nWe evaluated performance by applying wavelength-specific image attenuation\nbased on established atmospheric transmittance models (Optics Express, 2011, 19(4):\n3379-3386), simulating visibility conditions of good visibility (100 km) and low\nvisibility (1 km) (Figure R26). Under good visibility (100 km), DVS demonstrates\nexcellent performance: achieving 100% accuracy with both HOTS (Vis DVS) and\nEVFlowNet (Vis DVS), comparable to our photosynapse array’s 100% accuracy with\nphotosynapse CNN (Vis+MIR). This confirms DVS’s superior performance under\ngood visibility conditions. However, performance diverges as visibility degrades, As\n54\nshown in Figure R26b. Under low visibility conditions, DVS-based methods\nexperience clear accuracy drops (68.8 for EVFlowNet, 66.7 for HOTS, 79.2 for\nDVS+RGB), while our Vis+MIR photosynapse array demonstrates better robustness\nwith accuracy remaining at 97.9%. These results demonstrate the advantage of\nVis+MIR fusion over DVS-only approaches in harsh atmospheric conditions.\nThese results can be attributed to the fact that while DVS detects logarithmic\nintensity changes rather than absolute intensity and benefits from exponential decay\nkernel circuitry, its core performance still relies on sufficient contrast variations within\nthe visible spectrum. According to atmospheric scattering theory, visible light\ntransmittance drops dramatically compared with MIR under low visibility conditions\n(Optics Express, 2011, 19(4): 3379-3386). This degradation not only reduces absolute\nlight intensity but also diminishes scene contrast. Even contrast-sensitive DVS\nstruggles to capture effective signals under such conditions (IEEE Transactions on\nPattern Analysis and Machine Intelligence (TPAMI), 2022, 45(1): 785-795). As the\nreduced contrast falls below the detection threshold, the noise-triggered event rate\nincreases dramatically due to photon shot noise and dark current contributions. While\nincreasing the event generation threshold can suppress noise-triggered events, this\ncomes at the cost of reduced sensitivity to genuine intensity changes, preventing the\nDVS from achieving the same imaging fidelity as under well-lit conditions (IEEE\nTransactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022, 45(1):\n785-795; Computer Vision – ECCV 2022, Part VII. vol. 13667).\nThese analyses position our photosynapse array as complementary to DVS rather\nthan competing. DVS possesses impressive advantages in temporal resolution and\nevent-driven characteristics within its operating visible spectrum, while our approach\nextends neuromorphic sensing capabilities to harsh environments through wide-spectral\nintegration. We have incorporated these detailed comparison results, and discussion\ninto the main text and Supplementary Figure S29 and Note S11, comprehensively\ndemonstrating the characteristics and application scenarios of both technological\napproaches.\n(a, Figure S28a in revised manuscript)) Atmospheric transmittance as a function of\nvisibility for visible (532 nm) and mid-infrared (10.6 μm) wavelengths. (b, Figure S29\nin revised manuscript) Recognition accuracy comparison between the photosynapse\narray (Vis+MIR) and DVS-based methods (EVFlowNet, HOTS, and DVS+RGB fusion)\nunder good visibility (100 km) and low visibility (1 km) conditions.\nFigure R27. Comparison of photosynapse array and DVS imaging. (a) Direct output\nframe from the photosynapse array. (b) Reconstructed DVS image incorporating\ntemporal event history under good lighting conditions.\nRevision.\nWe further evaluated performance comparison of our photosynapse array with\nDVS cameras69,73–75 (see Note S11 for details). Under good visibility (100 km), both\nsystems achieve comparable performance (Figure S29). However, under low visibility\nconditions (1 km), DVS-based methods experience significant performance\ndegradation (accuracy drops to 66.7%~79.2%), as atmospheric attenuation reduces the\ncontrast in visible wavelengths that DVS sensors rely on for event generation76,77. In\ncontrast, our photosynapse array with fused Visible and MIR maintains 97.9% accuracy\n56\nby utilizing the better penetration of MIR wavelengths through atmospheric obscurants.\nThis demonstrates that the wide-spectrum photosynapse array can complement visible-\nonly DVS systems by maintaining robust classification in challenging conditions,\nsuggesting potential for combined systems that utilize both technologies' strengths in\nfuture applications.\nReferences.\n73. Grabner, M. & Kvicera, V. The wavelength dependent model of extinction in\nfog and haze for free space optical communication. Opt. Express 19, 3379 (2011).\n74. Zhu, A. Z., Yuan, L., Chaney, K. & Daniilidis, K. EV-FlowNet: Self-\nSupervised Optical Flow Estimation for Event-based Cameras. in Robotics: Science\nand Systems XIV (2018). doi:10.15607/RSS.2018.XIV.062.\n75. Hu, Y., Liu, S.-C. & Delbruck, T. v2e: From Video Frames to Realistic DVS\nEvents.\n76. Guo, S. & Delbruck, T. Low Cost and Latency Event Camera Background\nActivity Denoising. IEEE Trans. Pattern Anal. Mach. Intell. 45, 785–795 (2023).\n77. Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel,\nOctober 23–27, 2022, Proceedings, Part VII. vol. 13667 (Springer Nature Switzerland,\nCham, 2022).\nNote S11: Comparison with DVS-based Vision Systems.\nWe conducted comparisons using simulated DVS data via the v2e simulator25 to\nprovide controlled conditions. To simulate different visibility scenarios, we applied\nwavelength-specific atmospheric attenuation to the conventional sensor images (see\nNote S9) based on established transmittance models24. Specifically, we modeled good\nvisibility (100 km) and low visibility (1 km) conditions by applying corresponding\nattenuation coefficients to visible wavelengths (532 nm) and mid-infrared wavelengths\n(10.6 μm). The attenuated intensity images were then converted into event streams,\nmimicking DVS responses under different visibility conditions.\nTo ensure fair comparison, we configured all networks with comparable parameter\ncounts. EVFlowNet (24.6 K parameters) adapts the optical flow estimation network\n128×128×2 time-surface representations and replaces the original flow decoder with\nglobal average pooling, extracting high-level motion features through four\nconvolutional layers for classification. HOTS (22.5K parameters) implements a\nhierarchical time-surface feature extractor for event-based vision27. The model learns\nspatio-temporal prototypes across three layers through unsupervised clustering,\nproducing 1,792-dimensional features that are classified by a single hidden layer neural\nnetwork. DVS+RGB fusion (25.6K parameters) processes DVS events as 64×64×10\nvoxel grids and RGB frames through parallel convolutional branches, combining\nfeatures via fully connected layers for classification. Our Photosynapse CNN (22.5K\nparameters) processes fused Vis+MIR signals through three convolutional layers with\nbatch normalization, followed by global average pooling and classification layers.\nSI References.\n24. Grabner, M. & Kvicera, V. The wavelength dependent model of extinction in\nfog and haze for free space optical communication. Opt. Express 19, 3379 (2011).\n25. Hu, Y., Liu, S.-C. & Delbruck, T. v2e: From Video Frames to Realistic DVS\nEvents.\n26. Zhu, A. Z., Yuan, L., Chaney, K. & Daniilidis, K. EV-FlowNet: Self-\nSupervised Optical Flow Estimation for Event-based Cameras. in Robotics: Science\nand Systems XIV (2018). doi:10.15607/RSS.2018.XIV.062.\n27. Lagorce, X., Orchard, G., Galluppi, F., Shi, B. E. & Benosman, R. B. HOTS:\nA Hierarchy of Event-Based Time-Surfaces for Pattern Recognition. IEEE Trans.\nPattern Anal. Mach. Intell. 39, 1346–1359 (2017).\n58"}, {"round": 2, "reviewer_comments": {"reviewer_1": "All my concerns have been adequately addressed by the authors. I recommend the manuscript for publication in its present\nform.", "reviewer_2": "The authors answered my questions satisfactorily.", "reviewer_3": "The authors have addressed my previous concerns. It is suitable to be published now.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_5": "1. Specifying the gap between DVS and the presented UV/MIR capabilities in the Introduction as well as a short mention in\nthe Results was very helpful in articulating the advantage of this work in challenging visible settings\n2. Emphasizing the strength of sensor fusion with RGB camera, DVS, and the presented photosynaptic array is balanced\nand reasonable approach\n3. Further detail on the relatively high Moran's I index under dark conditions is an important clarification. The additional\nFigure R24 alongside the sentence on future potential improvements including optimized peripheral routing to minimize\nedge effects is fair.\n4. Shifting language from \"frequency-dependent photoresponse with temporal filtering properties\" to \"low-pass filtering\nproperties characteristic of biological synaptic dynamics\" is more precise and once again also appreciated\n5. Regarding appending of exponential decay kernels, the authors make a great point about extending this established\npractice to UV-to-MIR spectrum hardware. The authors motivate this decision based on past literature, and the revision is\nwell-received\n6. The authors provide an impressive and highly informative simulation of photosynapse array, DVS (EVFlowNet, HOTS),\nand DVS+RGB that crucially back up the claims/motivation made in the introduction regarding heightened performance in\nchallenging conditions. Great work here!\nNo further comments or questions."}, "author_response": ""}]

Decision letter