Retinocortical in-sensor neuromorphic vision platform for NIR-augmented artificial vision

Domain: neuroscience
Published: 2026-04-08
Editorial outcome: major_revision

Abstract
In-sensor neuromorphic vision platforms with near-infrared (NIR) sensitivity are essential for intelligent imaging in low-light and multispectral environments. Here, a retinocortical dual-mode platform based on evolved-synaptic transistor (Evo-SynT) devices using upconversion nanoparticles (UCNP) and their integration into evolved-retina optical synapse (EROS) arrays is introduced. Evo-SynT devices exhibit key synaptic features, including paired-pulse facilitation indices exceeding 183.93% and 136.36% at a 0.5 s interval under 808 nm and 940 nm illumination, respectively, and analog weight modulation across 512 conductance states. A 12×12 EROS array enables dual-mode operation: retinal-like in-sensor preprocessing and cortical-like in-memory classification. The EROS array improves pedestrian detection accuracy under low-light conditions from 0.7806 to 0.8481 (808 nm) and 0.9071 (940 nm), and achieves classification accuracies of 77.19% and 79.40%, respectively. These results highlight the EROS platform as a scalable solution for integrated NIR-sensitive neuromorphic vision systems.

Introduction
The human visual system processes optical information through a hierarchical retinocortical pathway, where photoreceptors in the retina perform early-stage analog signal preprocessing, and the visual cortex conducts higher-order feature extraction and cognitive interpretation, as shown in Fig. 1a. Inspired by this biological model, artificial vision systems are increasingly adopting architectures that emulate both retinal and cortical functions in hardware, bridging early signal acquisition with downstream intelligence.1-4 Such bioinspired retinocortical frameworks offer a promising foundation for developing next-generation vision computing platforms that operate beyond the visible spectrum. Indeed, while biological vision remains fundamentally constrained to visible wavelengths, artificial vision systems can exploit spectral regions such as near-infrared (NIR), which hold significant untapped potential for advanced perception.5-7

Extending artificial vision capabilities into the NIR range (~700-1000 nm) enables access to rich visual information beyond human perception. NIR light penetrates materials opaque to visible wavelengths, allowing imaging in low-light, dark, or visually obstructed conditions. This spectral advantage has driven the deployment of NIR-sensitive systems in fields such as autonomous driving, biomedical imaging, remote sensing, and security surveillance.8-11 However, while sensing in this regime is well established, the challenge lies in processing large volumes of data in real time. Conventional architectures, which separate sensing and computation, often suffer from latency and bandwidth limitations. In response, in-sensor vision computing platforms are emerging as an effective solution, co-locating sensing and processing to enable immediate, energy-efficient interpretation of NIR visual data.12-16

Realizing such integrated NIR sensing and processing platforms at the device level critically depends on the intrinsic capabilities of the underlying semiconductor materials. Various emerging semiconductor candidates for NIR in-sensor vision computing, such as two-dimensional (2D) semiconductors and organic compounds, offer appealing advantages including strong light–matter interactions, mechanical flexibility, and tunable spectral responses.17-20 However, despite these promising attributes, both 2D and organic semiconductors face critical drawbacks in achieving large-area integration, as well as stable and uniform operation—essential requirements for practical vision applications.21-23 As a result, meeting the practical requirements of NIR in-sensor vision systems has remained a significant challenge.

In light of these limitations, metal-oxide semiconductors have drawn renewed interest as an alternative material platform for in-sensor vision computing devices. Metal-oxide semiconductors offer high electronic performance, excellent uniformity over large-area substrates, and good environmental stability, directly addressing the scalability and stability challenges of 2D and organic semiconductors.24-30,48,49 However, most metal-oxide semiconductors possess wide bandgaps. For instance, indium gallium zinc oxide (IGZO) exhibits a band gap of approximately 3.3 eV, which makes it effectively unresponsive to NIR light. This inherent limitation means that metal-oxide-based sensors cannot directly detect NIR signals, representing a critical drawback for NIR in-sensor vision computing applications.

In this research, we demonstrate a metal-oxide-based in-sensor vision computing platform, termed the evolved-retina optical synapse (EROS) array, which enables direct NIR signal detection and subsequent image processing within the array. The EROS array consists of a 12×12 pixel matrix, with each pixel comprising an IGZO-based evolved-synaptic transistor (Evo-SynT)—in which upconversion nanoparticles (UCNPs) convert incident NIR photons into ultraviolet (UV) or visible wavelengths detectable by the metal- oxide semiconductor, as shown in Fig. 1b. This design endows each pixel with direct NIR sensitivity at 808 nm and 940 nm, enabling reliable image acquisition under low-light conditions beyond the visible spectrum. Furthermore, by leveraging the uniform, large-area deposition of metal-oxide-based electronics, the EROS array achieves consistent performance across all pixels, overcoming the scalability and uniformity limitations of prior 2D- or organic-based devices. As shown in Fig. 1c and Supplementary Fig. 1, this platform operates in dual modes—retina-like preprocessing for enhancing NIR imagery in low-visibility conditions, and cortex-like neuromorphic postprocessing for generating analog synaptic outputs suitable for subsequent neural network-based classification. These findings demonstrate a near-infrared sensitive in-sensor vision computing platform that integrates early-stage signal acquisition with downstream information processing, offering a viable route toward machine vision systems that operate beyond the spectral range of human perception.

Results
Upconversion enabled near infrared synaptic behavior
To enable NIR photoresponsivity in the Evo-SynT, UCNPs were employed as internal spectral converters. UCNPs are luminescent nanomaterials that convert NIR light into higher-energy UV or visible emissions through a multiphoton process known as photon upconversion.31 Typically built from a NaYF4 host lattice doped with Yb3+ sensitizers and activator ions such as Tm3+ or Er3+, UCNPs exhibit sharp emission peaks, long lifetimes, and high photostability.32,33 Among various UCNP candidates, NaYF4 co-doped with Yb and Tm (NaYF4:Yb/Tm) nanoparticles were introduced due to their ability to emit UV and blue photons, which spectrally overlap with the optical absorption range of IGZO, enabling efficient photocarrier generation under upconverted illumination.34 NaYF4:Yb/Tm nanoparticles were synthesized via thermal decomposition in oleic acid and 1-octadecene, a well-established route for producing monodisperse nanocrystals with high upconversion efficiency. Transmission electron microscopy (TEM) and dynamic light scattering (DLS) confirmed a uniform spherical morphology with an average diameter of 34 ± 1.3 nm (Fig. 2a and Supplementary Fig. 2 ). Elemental mapping using energy-dispersive X-ray spectroscopy (EDS) revealed homogeneous spatial distribution of Y, F, Yb, and Tm ions across the surface (Fig. 2d), with quantitative analysis indicating doping levels of 6.21% Yb and 0.99% Tm consistent with commonly used doping levels for efficient upconversion and minimal concentration quenching.35

To evaluate the upconversion property of UCNPs, photoluminescence (PL) spectrum of the synthesized NaYF4:Yb/Tm nanoparticles were measured under 980 nm NIR laser excitation at a power intensity of 500 mW cm-2, as shown in Fig. 2b. Under 980 nm excitation, the UCNPs exhibited sharp photoluminescence peaks at 344 nm (1I6 → 3F4), 360 nm (1D2 → 3H6), 450 nm (1D2 → 3F4), and 473 nm (1G4 → 3H6), which are characteristic of Tm3+ transitions.34 These emissions result from sequential energy transfer between Yb3+ sensitizers and Tm3+ activators. In this system, Yb3+ acts as a sensitizer: upon absorbing 980 nm photons, Yb3+ ions are excited from the 2F7/2 to the 2F5/2 level and subsequently transfer their energy to neighboring Tm3+ ions (Fig. 2c).36 These results confirm the upconversion functionality of the NaYF4:Yb/Tm nanoparticles, whereby absorbed NIR photons are converted into higher-energy emissions through multiphoton excitation processes.

Based on the verified optical functionality and structural uniformity of the NaYF4:Yb/Tm nanoparticles, the Evo-SynT device was fabricated by embedding the UCNPs between two IGZO active layers, as shown in Fig. 2e. The detailed fabrication process is described in Supplementary Fig. 3 and the methods section. This embedded structure maximizes photonic interaction within the active region while minimizing interfacial degradation, enhances photon-to-carrier conversion efficiency, and ensures long-term stability under optical stimulation by confining the upconverted emissions near the IGZO interface. UV–Vis–NIR absorption measurements further confirmed this optical enhancement, with the IGZO/UCNP/IGZO sandwich structure exhibiting substantially higher absorbance in the 808 and 940 nm regions compared to pristine IGZO film or IGZO/UCNP bilayers (Supplementary Fig. 4), validating the effectiveness of the vertical stacking strategy for NIR light harvesting.

To evaluate the device-level photoresponse of the upconversion-assisted Evo-SynT, transfer characteristics were measured under NIR irradiation at 808 nm and 940 nm with VDS of 10.1 V. As shown in Fig. 2f and 2g, the device exhibited increasing drain current and negative shift with rising optical power at both wavelengths, confirming NIR responsiveness. These observations verify that the integrated UCNPs play a central role in enabling deep NIR sensitivity in the Evo-SynT. To confirm that the upconverted light emitted by the UCNPs can be effectively absorbed by the IGZO channel, the optical absorption characteristics of IGZO were analyzed. As shown in Supplementary Fig. 5, the Tauc plot extracted from ultraviolet–visible (UV–vis) spectroscopy reveals an optical bandgap of approximately 3.21 eV, which corresponds to the absorption of photons in the ultraviolet and blue spectral regions. This result confirms that the upconverted emissions from the UCNPs fall within the absorbable energy range of the IGZO channel.

To quantify this energy transfer efficiency, we performed a spectral overlap–based analysis. We defined the optical coupling efficiency (ηcoupling) as the ratio of the absorbed photon flux to the total emitted flux, calculated by integrating the overlap between the UCNP emission spectrum and the IGZO spectral absorptance:
𝜂coupling = (∫ΦUCNP(𝜆) 𝐴IGZO(𝜆) 𝑑𝜆 / ∫ΦUCNP(𝜆) 𝑑𝜆) ×100 (%)

where ΦUCNP(𝜆) represents the UCNP photon flux spectrum and 𝐴IGZO(𝜆) is the wavelength dependent absorption probability of the IGZO channel.

This calculation yields an estimated ηcoupling of approximately 21.6%, confirming that the dominant NaYF4:Yb/Tm emission peaks (e.g., 290 nm, 345 nm, 360 nm) are effectively harvested by the active layer. Crucially, while typical NaYF4:Yb/Tm systems exhibit a modest internal upconversion quantum yield (UCQY) of 0.5–4%, the high optical coupling combined with the intrinsic photoconductive gain of the transistor compensates for the limited photon flux.32,33,52,53 The UV and blue photons generated by UCNPs possess sufficient energy to excite trap states associated with deep-level neutral oxygen vacancies (VO) within the IGZO channel. Upon photoexcitation, deep-level neutral VO centers are ionized into donor-like states such as VO+ and VO2+ near the conduction band, releasing electrons into the channel (e.g., VO0 → VO+ + e- or VO0 → VO2+ + 2e-), as shown in Supplementary Fig. 6. This process significantly increases channel conductivity under illumination. Furthermore, the ionized VO states are metastable, and their recombination is kinetically hindered, leading to a prolonged photocurrent decay even after the removal of light.37,38,50 This persistent photoconductance (PPC) behavior enables the device to retain optical memory, supporting its operation as a photo-synaptic element in neuromorphic vision systems. This PPC mechanism, involving VO ionization and slow recombination kinetics, is consistent with prior observations in IGZO, further validating the oxygen vacancy origin of PPC in Evo-SynT.51

To confirm that the NIR photoresponse originates specifically from the UCNP-mediated upconversion process, control devices without UCNPs or with only the dispersing solvent were tested, as shown in Supplementary Fig. 7. Both configurations showed negligible response under 808 nm and 940 nm illumination, validating that the observed photocurrent in Evo-SynT is exclusively attributed to the upconverted emissions from NaYF4:Yb/Tm nanoparticles. To further ensure that the observed photocurrent does not originate from thermal effects but exclusively from the upconversion process, we additionally conducted in-situ temperature measurements under prolonged 808 nm illumination at a power density of 1 W/mm2 for 10,000 s. The p+-Si/SiO2/IGZO was tested. The sample exhibited a temperature increase from 24.35 oC to 47.77 oC, as shown in Supplementary Fig. 8. Considering that typical measurement durations are on the order of 10 s, during which the temperature remained close to room temperature, these results confirm that the photoresponse is not thermally driven but is a direct consequence of UCNP-mediated upconversion.

To investigate the synaptic behavior of the upconversion-assisted Evo-SynT, its ability to retain photocurrent after pulsed optical stimulation was first examined. Postsynaptic current (PSC) measurements were performed under pulsed NIR illumination at 808 nm and 940 nm with VDS = 0.5 V. As shown in Fig. 2h and 2j, 808 nm and 940 nm light pulses were applied for 5 s, under varying power densities, and the drain current was monitored for 60 s following light-off. In both wavelength regimes, the device exhibited a power-dependent increase in peak PSC followed by a gradual decay, indicative of synaptic relaxation behavior. The inset graph in Fig. 2h and 2j shows the extracted PSC values measured 60 s after light-off for each power condition. To more clearly visualize the retention behavior, the PSC values at 70 s were plotted as a function of optical intensity in Fig. 2i and 2k. Starting from an initial dark current of approximately 0.05 nA, the PSC increased from 0.21 nA to 2.15 nA under 808 nm, and from 0.18 nA to 0.71 nA under 940 nm, as the power intensity increased from 13 mW mm-2 to 1325 mW mm-2. These results demonstrate that the Evo-SynT can function as a photo-synaptic element capable of encoding input strength into persistent electrical states, validating its potential for vision-inspired neuromorphic computing.

Demonstration of upconversion enabled synaptic functionality
To comprehensively evaluate the synaptic functionality of the upconversion-assisted Evo-SynT, a series of electrical measurements were performed under NIR optical stimulation. These include paired-pulse facilitation (PPF) to probe short-term plasticity (STP), transition from STP to long-term potentiation (LTP) under repeated stimuli, as well as reversible potentiation/depression cycles and conductance linearity—collectively demonstrating the device’s capacity to emulate essential forms of learning and memory at the hardware level.39,40

As an initial indicator of STP, PPF measurements were conducted under pulsed NIR illumination at 808 nm and 940 nm, as shown in Fig. 3a. PPF, a characteristic STP response, reflects enhanced PSC upon a second stimulus due to residual carrier effects. The PPF index is quantitatively defined as:
PPF index (%) = (A2/A1)×100

where A1 and A2 represent the amplitudes of the first and second postsynaptic current peaks, respectively. The device exhibited a maximum PPF index of 183.93% at 0.5 s under 808 nm, and 136.36% at the same interval under 940 nm. In both cases, the PPF index decayed exponentially with increasing inter-pulse interval (ΔT), consistent with biologically inspired facilitation dynamics. These findings confirm that the Evo-SynT reliably reproduces STP behavior under optical stimulation.

To explore the transition from STP to LTP, a fundamental mechanism underlying learning and memory, key stimulation parameters were systematically modulated under 808 nm and 940 nm excitation. First, the PSC was measured at increasing pulse frequencies while maintaining a constant light power density of 13 mW mm-2, revealing frequency dependent synaptic potentiation as shown in Fig. 3b and 3c. Under 808 nm, the PSC increased from 0.19 nA at 0.2 Hz to 0.61 nA at 1 Hz, while 940 nm excitation resulted in a rise from 0.06 nA to 0.23 nA. Next, varying the optical power density also enhanced PSC magnitude, reaching 4.24 nA for 808 nm and 0.69 nA for 940 nm at the highest intensity of 266 mW mm-2, as shown in Fig. 3d and 3e. Additionally, as shown in Supplementary Fig. 9a and 9b, increasing the number of pulses at fixed frequency and intensity led to a gradual shift from transient to sustained PSC, demonstrating long-term conductance retention. Quantitative analysis of the gain (An/A1) showed increasing values with both frequency and intensity, reaching maximum values of 4.48 for 808 nm and 2.82 for 940 nm at a higher frequency of 1 Hz, and values of 14.98 for 808 nm and 9.46 for 940 nm at 266 mW mm-2, as shown in Fig. 3f and 3g. These results confirm that Evo-SynT exhibits dynamic modulation of synaptic strength in response to optical stimulation, effectively emulating the transition from STP to LTP observed in biological synapses.

To further evaluate the synaptic functionality of the upconversion-assisted Evo-SynT, repetitive potentiation and depression experiments were carried out under both 808 nm and 940 nm excitation, focusing on the linearity, stability, and bidirectional tunability of conductance modulation. Under 808 nm illumination (13 mW mm-2, 1 Hz), the Evo-SynT exhibited a highly linear increase in conductance across 512 consecutive pulses (Fig. 3h and Supplementary Fig. 10a), demonstrating reliable potentiation with discrete state evolution. This was achieved without any gradual increase in light intensity, which is favorable for low power operation. A similarly robust potentiation trend was observed under 940 nm excitation (Fig. 3j and Supplementary Fig. 10b), confirming consistent behavior across wavelengths.

Depression behavior was investigated by applying a gate bias of 1.5 V for 808 nm and 1.0 V for 940 nm following the optical pulses. To justify these electrical depression conditions, we additionally performed single-pulse transient and relaxation measurements with systematically varied pulse amplitudes and durations, which are provided in the Supplementary Fig. 11. These measurements establish that a pulse duration of 0.5 s with gate biases of 1.5 V (for 808 nm) and 1.0 V (for 940 nm) yields reliable and controlled synaptic depression without inducing excessive over-erasure or instability. For the synaptic weight curves shown in Fig. 3i and 3k, both optical potentiation and electrical depression were implemented using pulse trains at 1 Hz. Optical pulses (808 or 940 nm, 13 mW mm-2) were applied to program LTP, and electrical gate pulses were used to induce long-term depression (LTD). In all cases, the PSC was read out 0.5 s after each pulse to suppress fast transient and capacitive components and to ensure that the extracted values represent quasi-steady synaptic weights, following the protocol commonly used in photonic synapse studies.

As illustrated in Fig. 3i and 3k, the Evo-SynT exhibited symmetric conductance modulation, with PSC values returning to baseline levels in a controlled and reversible manner. To quantitatively assess the linearity of synaptic weight modulation, a nonlinearity (NL) metric was introduced based on exponential fitting of the potentiation and depression characteristics. The weight update behavior under optical stimulation was modeled using the following exponential equations:
GP(t) = B(1-e^(-t/AP)) + GMIN
GD(t) = (GMAX-C)e^(-t/AD) + C

where GP(t) and GD(t) are the conductance during potentiation and depression at time t, respectively, t is the number of optical stimulation pulses, GMAX and GMIN are the maximum and minimum conductance values, respectively, AP and AD are the characteristic nonlinearity parameters for potentiation and depression, respectively, and B and C are fitting constants related to the scaling of conductance range and initial offset. The NL was then calculated as:
NL = tMAX / A

where tMAX is the total number of pulses. Smaller NL values correspond to slower and more linear updates.41,42 Under 808 nm illumination, AP was 360.59 and depression nonlinearity AD was 4290.07, resulting in NL values of 1.42 for potentiation and 0.093 for depression. For 940 nm, AP was 6.63 × 10^6 and AD was 13660.39, with NL values of 7.72 × 10^-5 for potentiation and 0.029 for depression. These findings confirm that the Evo-SynT enables highly linear and symmetric potentiation and depression behaviors across numerous conductance states, making it well suited for precise and scalable synaptic weight modulation in neuromorphic computing platforms.

To further verify that these multilevel synaptic states are not transient artifacts but represent physically stable states, retention measurements were conducted for both optically potentiated and electrically depressed levels, as shown in Supplementary Fig. 12 and 13.54 Distinct synaptic states were first programmed using defined numbers of optical or electrical pulses, and the postsynaptic current was read 10 s after the final pulse to suppress fast transient components. The PSC was then monitored for 100 s and analyzed by linear fitting to evaluate state stability. Using a state-separability criterion based on inter-level spacing, the optically programmed LTP states remained distinguishable for 57 s (64 pulses) up to 795 s (1 pulse) at 808 nm and 68 s (64 pulses) up to 803 s (1 pulse) at 940 nm. For electrical depression, the LTD states remained separable for 70 s (64 pulses) up to 600 s (1 pulse) at 1.5 V and 120 s (64 pulses) up to 900 s (1 pulse) at 1.0 V, confirming stable multilevel electrical erasure with voltage-dependent retention strength.

To evaluate the endurance of the programmed synaptic states under realistic learning conditions, prolonged optical–electrical cycling tests were performed. The Evo-SynT was repeatedly trained using alternating optical potentiation and electrical depression for a total of 1500 cycles, mimicking extended learning and forgetting processes, as shown in Supplementary Fig. 14. Throughout this endurance test, the devices maintained stable and controllable postsynaptic current modulation without abrupt degradation or loss of switching functionality, demonstrating that the UCNP-integrated heterostructure can withstand repeated photo-electrical stress. This result confirms that the multilevel synaptic states are not only retained over time but also preserved under long-term repeated operation, supporting the reliability of the Evo-SynT platform for neuromorphic optoelectronic systems.

These results collectively demonstrate that the Evo-SynT reliably emulates key synaptic behaviors—including STP and LTP, linear and reversible weight modulation, and frequency-dependent learning—under biologically inspired stimulation protocols. Unlike conventional phototransistors, which typically exhibit monotonic and intensity-dependent responses, Evo-SynT shows sensitivity to the temporal dynamics of optical pulses such as frequency, interval, and pulse count. This temporal dependence reflects neuromorphic functionality and distinguishes the Evo-SynT from simple phototransduction-based devices. A performance comparison with recently reported NIR synaptic devices (Supplementary Table 1) confirms that Evo-SynT outperforms existing technologies in terms of responsivity, energy efficiency, and recognition accuracy.

Array integration for in sensor neuromorphic vision
To validate the system-level applicability of the upconversion-assisted Evo-SynT, a 12 × 12-pixel EROS array was constructed on a glass substrate, where each pixel integrates an Evo-SynT and an IGZO-based switching transistor in a two-transistor (2T) configuration, as shown in Fig. 4a and Supplementary Fig. 15a. This architecture enables spatially distributed NIR sensing and synaptic processing across the matrix, supporting precise pixel-level readout and electrical isolation. The vertically stacked IGZO/UCNP/IGZO trilayer structure within each Evo-SynT enhances light absorption while minimizing lateral footprint, yielding a high optical aperture ratio of 70.2% and a geometric fill factor of 35.7%. These features collectively demonstrate a scalable, uniform, and energy-efficient hardware platform for in-sensor neuromorphic vision systems.43

To validate the device fabrication and functionality of the EROS array, photographic and microscope images in Fig. 4b–d and Supplementary Fig. 15b confirm the successful patterning and integration of the optical synaptic pixels, including clearly defined sensing and switching lines across the 12 × 12 matrix. The multilayered device architecture is further detailed in Supplementary Fig. 16, with complete fabrication procedures described in the Methods section. Pixel-wise electrical characterization was performed by applying a 1 V bias to the data line, sweeping the scan line voltage from −10 V to 10 V, and activating the switching transistors using a 5 V gate bias. As shown in Supplementary Fig. 17, the resulting sensing line current (ISL) exhibited uniform and stable characteristics across all pixels, confirming high device yield and reliable operation throughout the array.

To demonstrate neuromorphic functionality at the array level, a cyclic learning-relearning process (learn–forget–1st relearn–forget–2nd relearn) was applied to the EROS array under alternating NIR stimulation. As schematically illustrated in Fig. 4e, optical patterns corresponding to the letters “Y” and “S” were projected onto the array using 808 nm and 940 nm illumination, respectively. Following initial memory encoding, memory decay was induced via dark relaxation, and subsequent reactivation was achieved through relearning cycles. The array was measured using a passive matrix configuration, where conductance changes in individual pixels were read out via line-by-line electrical addressing under scan line voltage 1 V. During each learning and relearning phases, the optical stimulation was applied at a pulse frequency of 1 Hz with a power density of 13 mW mm-2. The 3D surface plot in Fig. 4f displays the ISL distribution across 42 pixels for the “Y” pattern under 808 nm excitation, corresponding to the spatial activation shown in Supplementary Fig. 18a. As shown in Supplementary Fig. 18b, all pixels exceeded the 0.3 nA threshold during initial learning (20 pulses), with reactivation requiring only 5-6 and 3-4 pulses in the first and second relearning cycles, respectively. Supplementary Fig. 19a shows median ISL evolution through dark relaxation (0.01 pA → 0.05 nA → 0.12 nA), while Supplementary Fig. 19b confirms reduced relearning pulse counts (16 → 2-3 → 1-2), collectively indicating stable and cumulative synaptic encoding. A comparable behavior was observed for the “S” pattern under 940 nm excitation, as shown in Fig. 4g and Supplementary Fig. 20a. Supplementary Fig. 20b demonstrates that all 46 addressed pixels exceeded the 0.15 nA threshold, and statistical summaries in Supplementary Fig. 21a and 21b further reveal a progressive increase in median ISL (0.002 nA → 0.040 nA → 0.048 nA) and reduction in pulse counts (17 → 3-4 → 1-2) over successive learning cycles. These results validate the EROS array’s ability to encode, retain, and rapidly recover spatially distributed memory through repeated optical stimulation, underscoring its capability for robust in-sensor learning.

While the current array demonstrates proof-of-concept functionality, its scalability toward high-resolution neuromorphic systems is further addressed in Supplementary Discussion 1, including design strategies to mitigate optical and electrical crosstalk, ensure addressability, and support flexible frame-rate operation. In addition, as shown in Supplementary Fig. 22, the operational reliability of the array under repeated optical–electrical synaptic cycling is quantitatively evaluated, demonstrating stable and reproducible conductance modulation over multiple write–erase sequences, which is essential for large-scale neuromorphic deployment.

Retinocortical near infrared neuromorphic vision computing
To demonstrate the practical utility of the EROS array for real-world vision computing applications, two simulation scenarios were explored: an in-sensor preprocessing task for NIR-based image enhancement under low-light conditions, and an in-memory post-processing task employing transformer-based classification.

As a first step, the in-sensor preprocessing task was implemented to enhance pedestrian detection under low-light conditions by leveraging the EROS array’s NIR sensitivity. As shown in Fig. 5a, the Low Light Visibility Infrared Pedestrian (LLVIP) dataset, containing challenging nighttime visible-light images, was selected to highlight the EROS array's ability to extend visual detection beyond human perception.44 Utilizing experimentally measured optoelectronic characteristics, the device response 𝐼(𝑥) under incident NIR intensity 𝑥 (mW mm-2) was modeled by a second-order polynomial:
𝐼(𝑥) = 𝑎𝑥^2 + 𝑏𝑥 + 𝑐

with fitted coefficients 𝑎 = 4.61×10^-16, 𝑏 = 1.03×10^-12, and 𝑐 = 6.51×10^-11 (808 nm) and 𝑎 = 7.52×10^-17, 𝑏 = 3.81×10^-13, and 𝑐 = 3.45×10^-11 (940 nm). Here, all 𝑥 values directly extracted from the experimental NIR power conditions applied to the physical Evo-SynT devices in EROS array. This approach ensures that the simulated image response is quantitatively grounded in real hardware behavior. For visual representation, raw data were normalized into 8-bit grayscale images via a logarithmic mapping:
𝐺(𝑥) = (log(1 + 𝐼(𝑥)) - log(1 + 𝐼min)) / (log(1 + 𝐼max) - log(1 + 𝐼min)) × 255

This preprocessing strategy, mimicking the neuromorphic visual processing of the EROS array, significantly enhanced pedestrian visibility under low illumination, as depicted in Fig. 5b-d and Supplementary Fig. 23. To quantify effectiveness, pedestrian detection was evaluated using the YOLOv8n model after 50 epochs of training. As shown in Fig. 5e-h, compared with baseline visible images (precision: 0.7806, recall: 0.6407, mean average precision at intersection over union (IoU) threshold 0.5 (mAP@0.5): 0.7190), the NIR-enhanced images under 808 nm illumination yielded marked improvements (precision: 0.8730, recall: 0.7502, mAP@0.5: 0.8481). Further enhancements at 940 nm provided optimal results (precision: 0.9071, recall: 0.8047, mAP@0.5: 0.8869). These findings validate the practical capability of the EROS array to perform intelligent, sensor-level image enhancement, emphasizing its potential as a preprocessing front-end in low-light vision computing systems.

Following the demonstration of EROS array capabilities in preprocessing, its application was further extended to transformer-based image classification using an in-memory computing framework. Employing the Tiny ImageNet dataset for benchmarking, a Vision Transformer (ViT) model was selected due to its complexity and suitability for evaluation (Fig. 6a).46 The experimentally measured potentiation and depression characteristics of the EROS array were mapped onto the transformer's multi-layer perceptron (MLP) synaptic weights, enabling analog weight encoding and low-power inference (Fig. 6b, Supplementary Fig. 24a and 24b). Synaptic programming errors were experimentally characterized for 10 devices under 808 nm and 940 nm illumination as shown in Supplementary Fig. 25a and 25b.47 Linear fitting yielded state-proportional error coefficients (α) of 0.0589 for 808 nm and 0.0767 for 940 nm, with both datasets achieving linearity (R2) greater than 0.9 and remaining within the acceptable error range (α < 8%). The extracted α-values served as direct inputs for the CrossSim inference simulations presented, thereby ensuring that the modeled synaptic variations reproduce experimentally observed programming errors. These results correspond to stable classification accuracy of 77.19% (808 nm) and 79.40% (940 nm), as shown in Fig. 6c–e. Confusion matrices under both wavelengths showed distinct diagonal dominance, indicating robust class discriminability as shown in Fig. 6f and 6g. To provide further granularity, Supplementary Fig. 26 presents class-wise accuracy distributions under both NIR conditions. Notably, the 808 nm case maintains higher accuracy across several classes (e.g., 98%, 96%, 94%), whereas the 940 nm case exhibits slightly more variation with a few lower-performing categories (e.g., 62%, 64%), yet still preserves reliable detectability overall.

The classification results were further analyzed using t-distributed stochastic neighbor embedding (t-SNE), revealing clearly separated semantic clusters under both illumination conditions as shown in Fig. 6h and 6i. These findings confirm that the EROS array maintains sufficient resolution and linearity of analog weights, effectively supporting transformer-based neuromorphic classification tasks. Overall, the EROS platform successfully demonstrates retinocortical functionality, serving both preprocessing and post-processing roles in integrated neuromorphic vision systems.

Methods
Synthesis of NIR responsive NaYF4:Yb/Tm core UCNP
Rare-earth lanthanide precursor solutions were prepared prior to chemical synthesis. YCl3 and YbCl3 were each dissolved in deionized water at a concentration of 1 M, while TmCl3 was prepared at 0.03 M. The solutions were vortexed and sonicated until fully clarified. For the NaOH/NH4F mixture, 0.10 g of NaOH was sonicated in 5 mL of methanol, followed by the addition of 0.148 g of NH4F under vigorous stirring. This solution was freshly prepared for each synthesis.

Under an argon atmosphere, 0.747 mL of YCl3, 0.25 mL of YbCl3, and 0.1 mL of TmCl3 were combined in a 100-mL three-necked round-bottom flask and heated to 110 °C for 15 minutes to evaporate residual water. A magnetic stir bar was used to maintain constant agitation at 300–400 rpm throughout the synthesis. Subsequently, 6 mL of oleic acid and 15 mL of 1-octadecene were added, and the mixture was heated to 150 °C until the solution became a clear pale yellow. After cooling to 60 °C, 5 mL of the methanol-based NaOH/NH4F mixture was introduced. The solution was then heated to 110 °C for 20 minutes to remove methanol, followed by a 10-minute vacuum step to eliminate any residual solvent. Complete removal of methanol and water at this stage is essential to prevent undesirable side reactions. Three consecutive cycles of vacuum and argon purging were conducted, each lasting one minute.

The temperature was increased to 300 °C and maintained for 1 hour under continuous stirring to complete the nanoparticle formation. Upon completion, the reaction mixture was cooled to room temperature and transferred to a 50 mL conical tube. The round-bottom flask was rinsed three times with acetone to recover any remaining particles. The total volume in the conical tube was adjusted to 40 mL with acetone and centrifuged at 6,654 g for 10 minutes. The resulting pellet was redispersed in 20 mL of hexane using vortex mixing. A final centrifugation at 1,000 g for 10 minutes was performed, and the clear hexane supernatant containing the nanoparticles was collected into a glass vial.

Fabrication of Evo-SynT
The Evo-SynT devices were fabricated using a three-terminal bottom-gate configuration. A heavily doped p⁺-Si wafer with a thermally grown 120-nm-thick SiO₂ layer served as both the substrate and the gate dielectric, with the underlying silicon functioning as the gate electrode. A 20 nm a-IGZO film was deposited on top of the SiO₂ layer by radio frequency (RF) magnetron sputtering at room temperature under a working pressure of 5 mTorr in argon ambient. The IGZO sputtering target consisted of In2O3:Ga2O3:ZnO in a 1:1:1 molar ratio.

Following the initial IGZO deposition, a layer of UCNPs (NaYF4:Yb/Tm core) was spin-coated onto the IGZO surface at 1000 rpm for 30 s. Subsequently, an additional 20 nm IGZO layer was deposited under identical sputtering conditions, encapsulating the UCNP layer. The stacked IGZO and UCNP layers underwent post-deposition annealing at 300 °C for 1 h and 100 °C for 30 min, respectively.

Finally, aluminium (Al) source and drain electrodes were defined via e-beam evaporation using a precision-aligned shadow mask to form well-defined channels. The resulting devices featured a channel width of 1000 μm and length of 150 μm.

Fabrication of EROS array
To demonstrate the EROS array, thin-film transistor arrays were fabricated on a glass substrate. A chromium (Cr)/gold (Au) (5/45 nm) gate electrode was deposited by thermal evaporation and patterned through a standard photolithography process. Subsequently, as gate insulator, a 55 nm-thick aluminium oxide (Al2O3) layer was deposited by atomic layer deposition process at 150 °C. The channel layer comprises upconversion nanoparticles embedded between two 20 nm-thick IGZO films. Both the top and bottom IGZO layers were deposited by RF magnetron sputtering using an IGZO sputtering target, patterned by photolithography and thermally annealed at 300 °C for 60 min. The UCNPs were spin-coated, patterned by lift off process and thermally annealed 100 °C for 30 min. The Al2O3 layer was patterned using photolithography to form contact holes. Lastly, as source and drain electrodes, a 75 nm-thick Al layer was deposited by thermal evaporation and patterned through a lift-off process. The fabricated TFTs had a channel width and length of 100 μm and 50 μm for Evo-SynT and 100 μm and 10 μm for switching TFT, respectively.

Characterization of UCNP
The characteristics of NaYF4:Yb/Tm core upconversion nanoparticles (UCNPs) were analyzed using transmission electron microscopy (JEM-F200, JEOL) and field emission scanning electron microscopy (JSM-7610F-Plus, JEOL), along with energy-dispersive X-ray spectroscopy mapping for elemental distribution. Hydrodynamic size was determined via dynamic light scattering using a SZ-100 system (Horiba). Upconversion luminescence spectra were obtained using a Fluorolog-QM photoluminescence spectrometer (Horiba) under excitation by a 980 nm laser.

Device characterization and simulation
The electrical properties of the Evo-SynT devices and EROS array were characterized at room temperature under dark conditions using a parameter analyzer (Model 4200A, Keithley Instruments). To investigate the NIR photoresponse, laser sources (Cubecell Technology) with wavelengths of 808 nm and 940 nm were employed. The incident power densities were systematically varied across seven levels: 13, 127, 255, 510, 764, 1019, and 1325 mW mm-2. To generate pulsed light stimuli, the lasers were modulated through a function/arbitrary waveform generator (UTG1010A, UNI-T), enabling controlled temporal stimulation of the synaptic devices.

To verify the presence and distribution of the NaYF4:Yb/Tm core UCNPs, field emission-scanning electron microscopy (FE-SEM) (JSM-7610F-Plus, JEOL) and energy-dispersive X-ray spectroscopy (EDS) mapping were performed. The upconversion luminescence behavior of the UCNPs was characterized using a photoluminescence spectrometer (FS5, Edinburgh Instruments) under 980 nm laser excitation at a power intensity of 500 mW cm-2. The optical bandgap of the IGZO layer was determined using ultraviolet–visible (UV–vis) spectroscopy (V-650, JASCO).

For neuromorphic performance evaluation, a two-stage artificial vision simulation was conducted. In the image pre-processing module, the Low-Light Visibility Infrared Pedestrian (LLVIP) dataset was used in conjunction with the You Only Look Once version 8 nano (YOLOv8n) architecture to enhance visibility under low-light NIR conditions. In the subsequent image classification stage, the Tiny ImageNet dataset was processed using a Vision Transformer (ViT) model within the CrossSim framework to assess the system’s classification accuracy and robustness under varying irradiation conditions.

Schematic illustrations
Schematic illustrations and 3D renderings were created while creating Figure 1a-c, 2c, 2e, 4a, 4e, 5a, 6a, and Supplementary Fig. 3, 6, 15a, 16 using Rhinoceros 3D and KeyShot, and further arranged using Microsoft PowerPoint (Microsoft Corporation).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript introduces the development of a retinal cortex neuromorphological vision platform based on upconversion nanoparticles (UCNPs) enhanced IGZO synaptic transistors, and its integration into a functional array for near-infrared (NIR) sensing and processing. This work demonstrates the dual-mode operation for image enhancement and classification. However, I found that this work lacks enough novelty. The mechanism explanation of performance is not perfect, and there is a lack of corresponding characterization. In addition, compared with existing neuromorphological vision systems, the proposed application performance does not represent significant progress. For these reasons, I believe that this work should not be published on Nature Communications. Here are some specific opinions.\n1.It is recommended to provide a systematic comparison of the absorption spectra of the IGZO thin film, the IGZO/UCNP hybrid structure, and the IGZO/UCNP/IGZO sandwich structure. At present, only the absorption data of the pure IGZO thin film are shown (Supplementary Fig. 4), which is insufficient to demonstrate the effective absorption of upconversion emission or to highlight the design advantage of the IGZO/UCNP/IGZO structure in enhancing light-harvesting efficiency.\n2.The authors are advised to carefully check the formatting consistency of all figures throughout the manuscript. Some figures currently show inconsistent fonts, font sizes, and line thicknesses. For example, multiple font sizes are present in Fig. 2h, and the labeling format for the wavelengths “808 nm” and “940 nm” in Figs. 3d and 3e is inconsistent. Standardizing labels, axes, and legends across all figures will significantly improve the clarity and professionalism of the manuscript.\n3.In Figs. 3i and 3k, the authors demonstrate synaptic weight depression behavior triggered by electrical pulses, which clearly supports the bidirectional tunability of the device. However, to more comprehensively characterize the electrical synaptic properties, it is suggested to include transfer curves with bidirectional gate voltage sweeps, as well as the transient response and relaxation behavior of the postsynaptic current under single electrical pulses of varying amplitude or duration.\n4.The reported near-infrared light power density used in the experiments reached up to 1325 mW/mm², and even the subsequent tests were performed at 13 mW/mm². The accuracy of these values should be clarified. Since practical applications (e.g., automotive night vision) impose strict limits on power consumption and illumination levels, it is recommended to provide device performance data under lower and more realistic power densities. Furthermore, quantifying or citing the upconversion efficiency of UCNPs would help to better evaluate the device performance and energy consumption in real-world conditions.\n5.The manuscript lacks statistical analysis of the performance uniformity and reliability of the 12 × 12 EROS array.\n6.To highlight the novelty and competitiveness of this work, it is suggested to include a performance comparison table, either in the main text or in the supplementary information, benchmarking the key parameters of this device against those of recently reported devices based on other technologies.\n7.In the frequency-dependent experiments presented in Figs. 3b and 3c, the total number of pulses applied varies with frequency. This makes it difficult to attribute the observed differences in excitatory postsynaptic current solely to frequency effects, as they may also arise from differences in the cumulative number of pulses.\n8.The description of the electrical testing method for the EROS array is not sufficiently clear. The authors mention that a patterned shadow mask was used for the learning–forgetting–relearning test. How, specifically, was the conductance of the array measured? A more detailed explanation is needed.", "reviewer_2": "This manuscript proposes a retinocortical in-sensor neuromorphic vision platform for NIR-augmented artificial vision. The work is somewhat intriguing and could be considered for potential publication in Nature Communications after appropriate revisions.\n1) Note that the NIR light power densities range from 13 to 1325 mW/mm2. The light irradiation generates a significant amount of heat that seriously affects the device performance. The authors should include a detailed discussion on the impact of such heat.\n2) No evidence is provided to support the assertion that the data in Fig. 3i,k indicate the device’s LTP behavior. Retention measurements of different states during the optical potentiation and electrical depression should be conducted, as reported in Ref. [R1]. Additionally, the device current decays over time for both LTP and LTD, indicating that the current data are not constant. This raises the question of how the current data in Fig. 3i,k were obtained. To the best of my knowledge, to date, only Ref. [R1] addresses this issue (see the caption of Fig. 4: “The memconductance values were measured 1 s after each light pulse” and “Top panel: The device was initially set to a relatively low memconductance of about 100 nS, and then ten SET operations were performed with different numbers of 420 nm light pulses. After each SET operation and subsequent retention measurement, the device was set to 100 nS again. Bottom panel: The device was initially set to a relatively high memconductance of about 400 nS, and then ten RESET operations were conducted with different numbers of 800 nm light pulses. After each RESET operation and subsequent retention measurement, the device was set to 400 nS again.”) Therefore, to enhance the scientific rigor of the manuscript, the authors are advised to include a detailed description of the method used to obtain the current data, as outlined in the aforementioned literature.\n3) PPC plays a crucial role in mimicking synaptic behaviors. The authors should include a comprehensive discussion on the device’s PPC mechanism. To enhance understanding of the PPC mechanism in oxides, the authors are encouraged to refer to Ref. [R2].\n[R1] Adv. Funct. Mater. 2021, 31, 2005582.\n[R2] Nat. Mater. 2012, 11, 301-305.", "reviewer_3": "This paper presents an exciting study that combines IGZO transistors with NaYF4:Yb/Tm upconversion nanoparticles to realize an in-sensor neuromorphic vision platform capable of sensing up to the near-infrared (NIR) range. The dual-mode (preprocessing-postprocessing) concept, which mimics the human retina-cortex architecture, is innovative, and the hardware-based demonstration of night vision demonstrates its potential applications. However, further verification is required for the actual photoconversion efficiency of the UCNP-IGZO combination, the identification of the fundamental mechanisms of synaptic activity, and the quantitative reproducibility of the YOLO simulation. Overall, the idea and system integration are excellent, but further experimental evidence and comparative analysis of the data will be necessary to fully support its publication value.\n-It is unclear how effectively the upconversion efficiency of NaYF4:Yb/Tm nanoparticles (especially when operated at 808 nm and 940 nm) couples with the actual absorption band of IGZO. A quantitative relationship between the absorption rate of photons emitted from UCNPs, the internal quantum efficiency, and the charge generation efficiency within the IGZO layer needs to be demonstrated experimentally or through simulation. Also physical mechanism should be reinforced.\n-In structures where UCNP layers are inserted between IGZO layers, thermal and chemical degradation, ion diffusion, and increased interfacial defects are highly likely to occur during prolonged light irradiation or electrical operation. It is necessary to evaluate whether this structural stability is maintained over long-term repeated operation (thousands of cycles or more), and to evaluate current drift, hysteresis, and fatigue phenomena.\n-Although PPC following NIR irradiation was interpreted as synaptic weight maintenance, it could also be explained by a simple phototransduction (trapping–detrapping) phenomenon. It appears that LTP was induced by simple changes in light intensity, without typical pulse-interval-dependent plasticity or voltage-dependent modulation. However, it is important to clarify how synaptic emulation and simple phototransistor operation were distinguished.\n-The presented 12 x12 array appears to be a proof-of-concept. System-level limitations, such as optical crosstalk, electrical interference, addressability, and frame rate, that will arise when scaling to higher resolutions are not discussed at all. Design solutions or circuit architecture proposals are needed to address these scalability issues.\n-While my paper highlights the advantages of the IGZO-based platform, a direct comparison of actual NIR sensitivity, response speed, SNR, power consumption, and other numerical features with existing 2D materials, perovskite, and organic phototransistor-based NIR devices was not conducted. Performance benchmarking (responsivity, detectivity, retention time) is necessary to truly evaluate the competitiveness of the proposed structure.\n-For image detection simulation work, it is unclear whether the data is obtained from actual hardware or is a simple function-fitting-based simulation, and its performance advantage over software has not been clearly proven."}, "author_response": "Response to the reviewers’ comments\nNature Communications\nPrevious Manuscript ID: NCOMMS-25-72858-T\nRetinocortical in-sensor vision neuromorphic platform for NIR-\naugmented artificial vision\nCorresponding author:\nHyun Jae Kim\nFull mailing address:\nSchool of Electrical and Electronic Engineering,\nYonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea\nTelephone:\n82-2-2123-5865\nE-mail address:\nhjk3@yonsei.ac.kr\nReviewer #1 Comments:\nThis manuscript introduces the development of a retinal cortex neuromorphological vision platform\nbased on upconversion nanoparticles (UCNPs) enhanced IGZO synaptic transistors, and its integration\ninto a functional array for near-infrared (NIR) sensing and processing. This work demonstrates the dual-\nmode operation for image enhancement and classification. However, I found that this work lacks\nenough novelty. The mechanism explanation of performance is not perfect, and there is a lack of\ncorresponding characterization. In addition, compared with existing neuromorphological vision\nsystems, the proposed application performance does not represent significant progress. For these\nreasons, I believe that this work should not be published on Nature Communications. Here are some\nspecific opinions.\n1. It is recommended to provide a systematic comparison of the absorption spectra of the IGZO thin\nfilm, the IGZO/UCNP hybrid structure, and the IGZO/UCNP/IGZO sandwich structure. At present,\nonly the absorption data of the pure IGZO thin film are shown (Supplementary Fig. 4), which is\ninsufficient to demonstrate the effective absorption of upconversion emission or to highlight the design\nadvantage of the IGZO/UCNP/IGZO structure in enhancing light-harvesting efficiency.\n2. The authors are advised to carefully check the formatting consistency of all figures throughout the\nmanuscript. Some figures currently show inconsistent fonts, font sizes, and line thicknesses. For\nexample, multiple font sizes are present in Fig. 2h, and the labeling format for the wavelengths “808\nnm” and “940 nm” in Figs. 3d and 3e is inconsistent. Standardizing labels, axes, and legends across all\nfigures will significantly improve the clarity and professionalism of the manuscript.\n3. In Figs. 3i and 3k, the authors demonstrate synaptic weight depression behavior triggered by electrical\npulses, which clearly supports the bidirectional tunability of the device. However, to more\ncomprehensively characterize the electrical synaptic properties, it is suggested to include transfer curves\nwith bidirectional gate voltage sweeps, as well as the transient response and relaxation behavior of the\npostsynaptic current under single electrical pulses of varying amplitude or duration.\n4. The reported near-infrared light power density used in the experiments reached up to 1325 mW/mm²,\nand even the subsequent tests were performed at 13 mW/mm². The accuracy of these values should be\nclarified. Since practical applications (e.g., automotive night vision) impose strict limits on power\nconsumption and illumination levels, it is recommended to provide device performance data under\nlower and more realistic power densities. Furthermore, quantifying or citing the upconversion efficiency\nof UCNPs would help to better evaluate the device performance and energy consumption in real-world\nconditions.\n5. The manuscript lacks statistical analysis of the performance uniformity and reliability of the 12 × 12\nEROS array.\n6. To highlight the novelty and competitiveness of this work, it is suggested to include a performance\ncomparison table, either in the main text or in the supplementary information, benchmarking the key\nparameters of this device against those of recently reported devices based on other technologies.\n7. In the frequency-dependent experiments presented in Figs. 3b and 3c, the total number of pulses\napplied varies with frequency. This makes it difficult to attribute the observed differences in excitatory\npostsynaptic current solely to frequency effects, as they may also arise from differences in the\ncumulative number of pulses.\n8. The description of the electrical testing method for the EROS array is not sufficiently clear. The\nauthors mention that a patterned shadow mask was used for the learning–forgetting–relearning test.\nHow, specifically, was the conductance of the array measured? A more detailed explanation is needed.\nResponse to the reviewer #1 comments\nThis manuscript introduces the development of a retinal cortex neuromorphological vision platform\nbased on upconversion nanoparticles (UCNPs) enhanced IGZO synaptic transistors, and its integration\ninto a functional array for near-infrared (NIR) sensing and processing. This work demonstrates the\ndual-mode operation for image enhancement and classification. However, I found that this work lacks\nenough novelty. The mechanism explanation of performance is not perfect, and there is a lack of\ncorresponding characterization. In addition, compared with existing neuromorphological vision\nsystems, the proposed application performance does not represent significant progress. For these\nreasons, I believe that this work should not be published on Nature Communications. Here are some\nspecific opinions.\n We sincerely thank the reviewer for the careful evaluation of our manuscript and for raising\nimportant points regarding novelty, mechanism clarification, and benchmarking. We would like to\nclarify that the novelty of this work can be clearly established from 4 complementary perspectives.\n1) Conceptual novelty: rarity of NIR-responsive IGZO synaptic transistors.\nIGZO thin-film transistors possess a wide bandgap (~3.3 eV), making them intrinsically insensitive\nto NIR photons. As a result, only a very limited number of studies (~15 journal papers) have reported\nIGZO-based TFTs capable of NIR photodetection, and almost all of these works focus solely on\nphotodetector functionality. Importantly, among this small body of literature, demonstrations of\nsynaptic functions such as multi-level plasticity, long-term memory, or bidirectional weight\nmodulation under NIR stimulation are extremely rare. To the best of our knowledge, this work\nrepresents the first demonstration of an IGZO-based NIR (~940 nm) phototransistor that\nsimultaneously exhibits synaptic plasticity, analog memory, and array-level operation, establishing a\nnew device class rather than an incremental extension of existing photodetectors.\n2) Mechanistic novelty: UCNP-mediated wavelength conversion instead of heterojunction\ncharge transfer.\nPrevious NIR-sensitive IGZO phototransistors rely almost exclusively on hybrid or heterojunction\narchitectures, where NIR-active absorbers—such as PbS quantum dots, perovskites, other narrow-\nbandgap semiconductors, plasmonic nanoparticles, or 2D materials—are directly interfaced with\nIGZO to enable interfacial charge transfer. In contrast, our work introduces a fundamentally different\ndetection mechanism, in which NIR photons are first upconverted by UCNPs into blue/UV photons\nthat are then absorbed by the IGZO channel itself. This wavelength-conversion strategy enables direct\nIGZO photogeneration without interfacial carrier injections, eliminating heterojunction instability\nissues and representing the first report of UCNP-enabled NIR sensing in an IGZO synaptic transistor.\n3) Quantitative novelty: competitive and benchmarked performance across material\nplatforms.\nBeyond conceptual advances, our device achieves strong quantitative performance, including\nhigh responsivity (116.37 A/W at 808 nm and 57.48 A/W at 940 nm), 512 analog conductance\nstates, and low energy consumption (77.7 pJ at 808 nm and 25.03 pJ at 940 nm). To place these\nresults in context, we have added a comprehensive benchmarking table comparing our IGZO-\nbased platform with state-of-the-art NIR neuromorphic devices based on oxide, 2D, organic, and\nperovskite materials. This quantitative comparison demonstrates that the proposed device\nachieves a rare combination of NIR sensitivity, analog synaptic resolution, and energy efficiency,\nwhich is not simultaneously realized in prior systems.\n4) System-level novelty: scalable array integration and functional application.\nFinally, the proposed platform goes beyond single-device demonstrations by integrating a 12 ×\n12 synaptic transistor array (288 devices) and validating its statistical reliability, uniformity, and\nendurance. This array is further used to demonstrate NIR neuromorphic vision tasks, achieving\n84.81% and 90.71% accuracy on the LLVIP dataset under 808 nm and 940 nm illumination,\nrespectively. Such a complete chain—from NIR sensing and synaptic memory to array-level\nprocessing and application-level performance—has not been demonstrated in prior IGZO-based\nor UCNP-assisted neuromorphic systems.\nFor mechanism explanation, we have significantly strengthened the mechanism analysis by adding\nboth quantitative metrics and complementary experimental characterizations, thereby clarifying the\nphysical origin of the observed NIR synaptic behavior.\nFirst, we have added the upconversion quantum yield (UCQY) of the UCNPs and explicitly\nincorporated this value into the device-level mechanism analysis. Based on the measured UCQY, we\nfurther calculated the optical coupling efficiency between the UCNP emission and the IGZO\nchannel, enabling us to quantify how efficiently NIR photons are converted into IGZO-absorbable\nphotons. This allows us to move beyond a qualitative description of upconversion-assisted sensing and\ninstead provide a quantitative link between NIR excitation and IGZO photogeneration.\nSecond, to bridge optical conversion with electrical response, we newly calculated the effective\nphoton-to-electron conversion factor of the device. This parameter directly connects the number of\nNIR photons incident on the device to the number of electrons contributing to the synaptic current,\nproviding a quantitative measure of the overall optoelectronic transduction pathway from UCNP\nupconversion to IGZO conduction. By combining UCQY, optical coupling efficiency, effective photon-\nto-electron conversion factor, the full mechanism is now supported by a closed, quantitative framework,\nrather than by phenomenological interpretation alone.\nThird, we added UV–Vis–NIR absorption spectra for each device configuration. These data directly\nverify the spectral matching between UCNP emission and IGZO absorption and confirm that the\nobserved NIR response cannot arise from IGZO alone. This spectral evidence further validates the\nproposed upconversion-mediated mechanism.\nFinally, we have expanded the discussion by incorporating newly added, recent journal references that\nreport related upconversion-assisted photodetection and oxide-based synaptic mechanisms, allowing\nthe proposed mechanism to be placed in a broader and more rigorous context.\nWe respectfully note that the concerns raised by the reviewer regarding novelty, mechanism, and\nbenchmarking have been fully and systematically addressed in this revision. Specifically, we have\nclarified the unique novelty of this work, strengthened the mechanistic explanation with new\nquantitative and spectroscopic characterizations, and added comprehensive benchmarking and ablation\nanalyses to contextualize the application-level performance. We therefore kindly ask the reviewer to\nreconsider the evaluation of this manuscript based on the revised version, which now provides a\ncomplete and rigorously supported presentation of the proposed NIR neuromorphic vision platform.\nComment 1: It is recommended to provide a systematic comparison of the absorption spectra of the\nIGZO thin film, the IGZO/UCNP hybrid structure, and the IGZO/UCNP/IGZO sandwich structure. At\npresent, only the absorption data of the pure IGZO thin film are shown (Supplementary Fig. 4), which\nis insufficient to demonstrate the effective absorption of upconversion emission or to highlight the\ndesign advantage of the IGZO/UCNP/IGZO structure in enhancing light-harvesting efficiency.\nReply\n We agree with the reviewer that a comparative analysis of the absorption characteristics across\ndifferent structural configurations is important to clarify the optoelectronic advantage of the\nIGZO/UCNP/IGZO architecture. In response, we have newly conducted UV–Vis–NIR absorption\nmeasurements on three structures: (i) pristine IGZO thin film, (ii) IGZO/UCNP bilayer, and (iii)\nIGZO/UCNP/IGZO trilayer, to make a systematic comparison of the absorption spectra under NIR\nregion. The results are presented in Fig. R1.\nAs shown, the pristine IGZO layer exhibits negligible absorbance at 808 nm and 940 nm, consistent\nwith its wide bandgap nature and minimal interaction with NIR photons. In contrast, both hybrid\nstructures incorporating UCNPs show clear absorption features at these wavelengths, verifying their\ncapacity to harvest NIR light. Notably, the IGZO/UCNP/IGZO sandwich structure displays enhanced\nabsorbance over the IGZO/UCNP bilayer, attributable to increased optical confinement and improved\nvertical coupling with the UCNP layer. These findings confirm that the designed trilayer architecture\nsignificantly improves the NIR light-harvesting capability.\nBeyond optical efficiency, the trilayer design also addresses device performance and fabrication\nchallenges. In the bilayer IGZO/UCNP structure, UCNP particles are positioned at the IGZO–electrode\ninterface, which interferes with uniform carrier injection and transistor operation due to surface\nroughness and nonuniform contact morphology.\nBy contrast, the IGZO/UCNP/IGZO configuration separates the UCNPs from the electrical contacts,\nallowing the top and bottom IGZO layers to serve as both optical sensitizers and stable transistor\nchannels. This vertically stacked geometry ensures direct IGZO–electrode contact while still enabling\nphoton absorption via upconverted emission from the embedded UCNPs. Similar multilayered hybrid\nconfigurations have been shown to improve both light-matter interaction and carrier transport in\nadvanced phototransistor designs.1-3\nWe have added comparative absorption data in supplementary information and incorporated a\nstructural benchmarking discussion into the revised manuscript to clarify the design advantages of the\nIGZO/UCNP/IGZO configuration.\nFig. R1. NIR absorbance spectra of three structures: (i) pristine IGZO thin film, (ii) IGZO/UCNP\nbilayer, and (iii) IGZO/UCNP/IGZO trilayer.\nReference\nImage Sensors. ACS Appl. Mater. Interfaces 17, 3620-3630 (2025).\nlevel-contact hybrid sandwich structure. J. Mater. Chem. C 13, 902-909 (2025).\nHeterojunction. ACS Photonics 10, 790-800 (2023).\nRevisions in the manuscript\n(1) Page 6\n[Newly added text]\nUV–Vis–NIR absorption measurements further confirmed this optical enhancement, with the\nIGZO/UCNP/IGZO sandwich structure exhibiting substantially higher absorbance in the 808 and\n940 nm regions compared to pristine IGZO film or IGZO/UCNP bilayers (Supplementary Fig. 4),\nvalidating the effectiveness of the vertical stacking strategy for NIR light harvesting.\n(2) Supplementary Information\n[Newly added figure]\nSupplementary Fig. 4. NIR absorbance spectra of three structures: (i) pristine IGZO thin film, (ii)\nIGZO/UCNP bilayer, and (iii) IGZO/UCNP/IGZO trilayer.\nComment 2: The authors are advised to carefully check the formatting consistency of all figures\nthroughout the manuscript. Some figures currently show inconsistent fonts, font sizes, and line\nthicknesses. For example, multiple font sizes are present in Fig. 2h, and the labeling format for the\nwavelengths “808 nm” and “940 nm” in Figs. 3d and 3e is inconsistent. Standardizing labels, axes,\nand legends across all figures will significantly improve the clarity and professionalism of the\nmanuscript.\nReply\n Following the reviewer’s comment, all figures throughout the manuscript have been\ncomprehensively revised to ensure consistent formatting. Specifically:\n1) All axis labels, tick labels, and legends were standardized to uniform font type and size.\n2) Line widths and marker dimensions were unified across all plots.\n3) The multiple font sizes previously observed in Fig. 2h and 2j have been corrected to a single\nuniform font specification.\n4) The wavelength labels “808 nm” and “940 nm” were reformatted to identical text style and\nalignment in Figs. 3d, 3e and Supplementary Fig. 18b.\nAll revised figures have been updated in both the main manuscript and the Supplementary Information,\nwith consistent formatting applied throughout to enhance visual clarity and professionalism.\nRevisions in the manuscript\n(1) Page 25\n[Before revision]\nFig. 2. Time-dependent synaptic current responses of the Evo-SynT under h, 808 nm and j, 940 nm NIR\nillumination\n[After revision]\nFig. 2. Time-dependent synaptic current responses of the Evo-SynT under h, 808 nm and j, 940 nm NIR\nillumination\n(2) Page 27\n[Before revision]\nFig. 3. Pulse intensity–dependent synaptic behavior at fixed frequency (1 Hz), with PSC increasing with\npower (13 to 266 mW/mm2) under d, 808 nm and e, 940 nm.\n[After revision]\nFig. 3. Pulse intensity–dependent synaptic behavior at fixed frequency (1 Hz), with PSC increasing with\npower (13 to 266 mW/mm2) under d, 808 nm and e, 940 nm.\n(3) Supplementary Information\n[Before revision]\nSupplementary Fig. 18. b, Individual ISL evolution profiles across three learning cycles under 808 nm\nillumination.\n[After revision]\nSupplementary Fig. 18. b, Individual ISL evolution profiles across three learning cycles under 808 nm\nillumination.\nComment 3: In Figs. 3i and 3k, the authors demonstrate synaptic weight depression behavior triggered\nby electrical pulses, which clearly supports the bidirectional tunability of the device. However, to more\ncomprehensively characterize the electrical synaptic properties, it is suggested to include transfer\ncurves with bidirectional gate voltage sweeps, as well as the transient response and relaxation behavior\nof the postsynaptic current under single electrical pulses of varying amplitude or duration.\nReply\n In response to the reviewer’s request, we have now newly added electrical synaptic properties\nincluding transfer curve with bidirectional gate voltage sweeps, transient response, and relaxation\nbehavior of the postsynaptic current to further strengthen the physical understanding and completeness\nof the device operation.\n1) Transfer curve with bidirectional gate voltage sweep:\nWe have measured transfer curve with bidirectional gate voltage sweep, which are now\nprovided in Fig. R2. The device exhibits a clear clockwise hysteresis loop with a positive\nthreshold-voltage shift between forward and reverse sweeps, which is characteristic of IGZO-\nbased TFTs. The extracted threshold-voltage shift (~1 V) is comparable to that of pristine\nIGZO TFTs and is consistent with trap-mediated charge storage associated with oxygen-\nvacancy-related states under electrical bias.1,2\nImportantly, however, electrical pulses alone do not generate or modulate a synaptic state in\nour device. Synaptic depression is observed only after prior optical potentiation, indicating\nthat the electrical response is gated by an optically written memory rather than by intrinsic\nIGZO hysteresis. Thus, while the bidirectional transfer curves confirm normal IGZO TFT\nbehavior, the transient electrical depression verifies the unique photo-enabled synaptic\noperation.\nFig. R2. Transfer characteristics of Evo-SynT under bidirectional gate voltage sweep.\n2) Electrical transient and relaxation behavior under single electrical pulse:\nWe additionally measured the postsynaptic current transient and relaxation behavior under\nsingle electrical depression pulses with systematically varied amplitude and duration.\nSpecifically, the device was first optically potentiated by 808 nm illumination at 13 mW/mm2\nfor 10 s, followed by a 10 s relaxation period before applying the electrical depression pulse.\n(i) Varying electrical pulse amplitude (pulse width fixed at 1 s)\nElectrical depression pulses of 1.0, 1.5, and 2.0 V were applied, and the corresponding\npostsynaptic current evolution is shown in Fig. R3. The results show a clear and systematic\ndependence on pulse amplitude.\nWhen a 1.0 V electrical pulse was applied, the postsynaptic current exhibited a moderate\ndecrease, dropping to approximately 0.11 nA immediately after the pulse and then slowly\nrelaxing to a stable value of about 0.10 nA after 70 s, indicating weak but sustained synaptic\ndepression. Increasing the pulse amplitude to 1.5 V induced a stronger depression, with the\ncurrent decreasing to around 0.08 nA right after the pulse and gradually relaxing to\napproximately 0.07 nA over 70 s, demonstrating enhanced electrical erase strength. At a higher\namplitude of 2.0 V, a much more pronounced response was observed. The current initially\ndropped sharply to about 0.02 nA, followed by a partial recovery and saturation near 0.05 nA,\nbefore finally stabilizing at approximately 0.04 nA. This non-monotonic transient behavior\nreflects the strong electric-field–induced redistribution of optically stored charges, followed\nby re-equilibration of shallow and deep trap states.\nOverall, the monotonic decrease of the residual postsynaptic current with increasing pulse\namplitude demonstrates voltage-controlled depression strength, fully consistent with synaptic\nweight modulation in oxide-based neuromorphic transistors.\nFig. R3. Electrical synaptic depression and relaxation of Evo-SynT under single-pulse voltage\nstimulation with varying amplitude.\n(ii) Varying electrical pulse duration (amplitude fixed at 1.5 V)\nWe further varied the pulse width to 0.5 s, 1.0 s, and 1.5 s, while keeping the pulse amplitude\nconstant at 1.5 V. The results are shown in Fig. R4.\nWhen the electrical pulse duration was set to 0.5 s, the postsynaptic current decreased\nmodestly to approximately 0.10 nA immediately after the pulse and then gradually relaxed to\nabout 0.09 nA, indicating weak electrical depression. Increasing the pulse duration to 1.0 s\nresulted in a stronger effect, with the current dropping to around 0.08 nA and subsequently\nrelaxing to approximately 0.07 nA, reflecting enhanced charge neutralization during the longer\nelectrical stimulus. For the longest pulse duration of 1.5 s, a much more pronounced response\nwas observed. The current initially decreased sharply to about 0.05 nA, followed by a partial\nrecovery to approximately 0.07 nA, and finally stabilized at around 0.06 nA. This behavior\nindicates that prolonged electric-field exposure induces deeper charge redistribution, followed\nby re-equilibration of trapped carriers in shallow and deep states as similarly shown in varying\nelectrical pulse amplitudes.\nFig. R4. Electrical synaptic depression and relaxation of Evo-SynT under single-pulse voltage\nstimulation with varying duration.\nElectrical transient and relaxation behavior under single electrical pulse are newly added in the\nsupplementary information to enhance the completeness and robustness of the manuscript.\nReference\n108459 (2022).\nby Controlling Measurement Speed. ECS Solid State Lett. 4, Q10 (2015).\nRevisions in the manuscript\n(1) Page 9\n[Newly added text]\nTo justify these electrical depression conditions, we additionally performed single-pulse transient and\nrelaxation measurements with systematically varied pulse amplitudes and durations, which are provided\nin the Supplementary Fig. 11. These measurements establish that a pulse duration of 0.5 s with gate\nbiases of 1.5 V (for 808 nm) and 1.0 V (for 940 nm) yields reliable and controlled synaptic depression\nwithout inducing excessive over-erasure or instability.\n(2) Supplementary Information\n[Newly added figure]\nSupplementary Fig. 11. Electrical synaptic depression and relaxation of Evo-SynT under single-pulse\nvoltage stimulation with a, varying amplitude and b, varying duration. The device was first optically\npotentiated by 808 nm illumination at 13 mW/mm2 for 10 s, followed by a 10 s relaxation period before\napplying the electrical depression pulse.\n+\nComment 4: The reported near-infrared light power density used in the experiments reached up to 1325\nmW/mm², and even the subsequent tests were performed at 13 mW/mm². The accuracy of these values\nshould be clarified. Since practical applications (e.g., automotive night vision) impose strict limits on\npower consumption and illumination levels, it is recommended to provide device performance data\nunder lower and more realistic power densities. Furthermore, quantifying or citing the upconversion\nefficiency of UCNPs would help to better evaluate the device performance and energy consumption in\nreal-world conditions.\nReply\n We appreciate the reviewer’s insightful comment regarding the realism and practicality of the\nreported illumination power densities. The power densities used in our experiments—ranging from\n13 mW/mm2 to 1325 mW/mm2—were derived based on direct calibration data provided by the laser\nsupplier (Cubecell Technology). The actual optical power outputs were measured at various driving\ncurrent percentages by Cubecell Technology as shown in Fig. R5, and the power densities were\ncalculated by dividing the absolute power by the laser spot area (diameter = 1 mm).\nFig. R5. Output power versus current percentage (P–I%) characteristics of NIR laser sources at a,\n808 nm and b, 940 nm, provided by Cubecell Technology.\nTo directly address the reviewer’s concern regarding low-power operation, we newly conducted\nadditional measurements at 6 mW/mm2. The results, as shown in Fig. R6, demonstrate that the device\nmaintains reliable photoresponse characteristics even under such reduced illumination, indicating its\nsuitability for low-power or ambient-light applications.\nv\nFig. R6. Postsynaptic current responses of Evo-SynT under 6 mW/mm2 and 13 mW/mm2 at a, 808 nm\nand b, 940 nm respectively.\nFor the upconversion efficiency, as established in existing literature, the typical internal upconversion\nquantum yield (UCQY) for NaYF :Yb/Tm core nanoparticles range from approximately 0.5% to 4%,\n4\ndepending on the excitation power density.2-6 We have explicitly included these references and\nquantitative information in the revised manuscript to provide a clear baseline.\nWe fully agree with the reviewer that real-world systems such as automotive night vision or portable\nbiometric sensors typically require minimal power consumption and operate under restricted light\nintensities. However, we note that the targeted application scenarios in our study—such as full-frame\nNIR preprocessing of urban-scale images and NIR-assisted neuromorphic classification—operate under\nactive illumination systems with dedicated NIR sources. In such contexts, illumination levels on the\norder of ~10 mW/mm2 are common and fall within the safe and practical bounds for NIR-emitting\ndiodes and vertical-cavity surface-emitting laser arrays, as demonstrated in prior work.1 Accordingly,\nthe power density used in our demonstration (13 mW/mm2) aligns closely with these practical levels,\nensuring relevance to the intended application domains. Under this operating condition, our EROS array\ndevice exhibits energy consumptions of 77.7 pJ (808 nm) and 25.03 pJ (940 nm) per synaptic event,\nrespectively, which fall well within a practically acceptable range for low-power neuromorphic and in-\nsensor vision applications. Nevertheless, we acknowledge the importance of validating device\nfunctionality under even lower illumination conditions for broader applicability. In future studies, we\nplan to extend our analysis toward sub-10 mW/mm2 operation regimes and tailor application scenarios\nto reflect such constraints.\nReference\nhttps://optics.org/news/9/9/25 (2018).\nytterbium-sensitized upconversion phosphors using the ytterbium (iii) emission as an internal\nstandard. Nanoscale 10, 17212-17226 (2018).\nnanocrystals. Chem. Soc. Rev. 38, 976-989 (2009).\nMater. 10, 968-973 (2011).\nenhanced stability for efficient photodynamic therapy. J. Nanobiotechnol. 18, 85 (2020).\nRevisions in the manuscript\n(1) Page 6\n[Newly added text]\nTo quantify this energy transfer efficiency, we performed a spectral overlap–based analysis. We defined\nthe optical coupling efficiency (η ) as the ratio of the absorbed photon flux to the total emitted flux,\ncoupling\ncalculated by integrating the overlap between the UCNP emission spectrum and the IGZO spectral\nabsorptance as shown in Supplementary Eq. 1. This calculation yields an estimated η of\ncoupling\napproximately 21.6%, confirming that the dominant NaYF :Yb/Tm emission peaks (e.g., 290 nm, 345\n4\nnm, 360 nm) are effectively harvested by the active layer. Crucially, while typical NaYF :Yb/Tm\n4\nsystems exhibit a modest internal upconversion quantum yield (UCQY) of 0.5–4%, the high optical\ncoupling combined with the intrinsic photoconductive gain of the transistor compensates for the limited\nphoton flux.32,33,52,53\n(2) Supplementary Information\n[Newly added equation]\nSupplementary Eq. 1. Optical Coupling Efficiency.\n∫Φ (𝜆) 𝐴 (𝜆) 𝑑𝜆\nUCNP IGZO\n𝜂 = ×100 (%)\ncoupling\n∫Φ (𝜆) 𝑑𝜆\nUCNP\nwhere Φ (𝜆) represents the UCNP photon flux spectrum and 𝐴 (𝜆) is the wavelength dependent\nUCNP IGZO\nabsorption probability of the IGZO channel.\n(3) References\n[Newly added references]\nytterbium-sensitized upconversion phosphors using the ytterbium (iii) emission as an internal standard.\nNanoscale 10, 17212-17226 (2018).\nenhanced stability for efficient photodynamic therapy. J. Nanobiotechnol. 18, 85 (2020).\nComment 5: The manuscript lacks statistical analysis of the performance uniformity and reliability of\nthe 12 × 12 EROS array.\nReply\n We recognize the importance of evaluating both performance uniformity and operational reliability\nin array-scale neuromorphic platforms. While the initial manuscript and supplementary information\nalready contain extensive statistical analysis addressing these aspects (revised Supplementary Fig. 17–\n21), we have now reorganized and newly added the relevant figures and descriptions to ensure clearer\npresentation.\n1) Performance uniformity\nElectrical uniformity was examined through pixel-wise I–V sweeps of all 144 pixels under\nidentical biasing conditions (1 V data line, −10 V to 10 V scan line, and 5 V gate activation;\nsee page 11 of the manuscript). The raw I–V characteristics are shown in Fig. R7a. To\nfacilitate statistical interpretation, we have newly added a histogram of the extracted readout\ncurrents from all pixels at 1V scan line, which is the main operating voltage of the EROS array,\nand its statistical data as shown in Fig. R7b and R7c.\nFor optical uniformity, two letters (“Y” and “S”) were applied, activating 42 and 46 pixels,\nrespectively. The individual photocurrent responses are plotted in Supplementary Fig.18b\nand 20b, while the corresponding statistical distributions are summarized in Supplementary\nFigs. 19 and 21. These results confirm consistent pixel-to-pixel behavior under uniform optical\nand electrical stimulation.\nFig. R7. a, I of the 12 × 12-pixel EROS array across all 144 pixels. b, Box plot showing the median\nSL\nand distribution of I values under scan line voltage 1 V. c, Individual I value under scan line voltage\nSL SL\n2) Operational reliability\nTo evaluate the operational reliability of the EROS array, we conducted repeated optical–\nelectrical cycling measurements on four representative pixels randomly selected across the 12\n× 12 array. Each pixel was subjected to two consecutive cycles of 20 optical potentiation pulses\nfollowed by 20 electrical depression pulses, emulating realistic synaptic write–erase operations.\nThe drain current was recorded for every cycle, and the mean values and standard deviations\nwere extracted and plotted as error bars, as shown in Fig. R8.\nThe results demonstrate that all four pixels exhibit highly reproducible conductance\nmodulation with minimal cycle-to-cycle variation, indicating stable synaptic operation under\nrepeated optoelectronic stimulation. The small standard deviations observed over multiple\npotentiation–depression cycles confirm that the array maintains reliable and repeatable\nsynaptic states without noticeable degradation or drift. These findings verify the robustness of\nthe EROS array against repetitive programming and erasing processes, which is a critical\nrequirement for practical neuromorphic vision systems.\nFig. R8. Cycle-to-cycle reliability of the EROS array under repeated optical potentiation and electrical\ndepression, with statistical evaluation across multiple pixels (number of pixels = 4).\nRevisions in the manuscript\n(1) Page 13\n[Newly added text]\nIn addition, as shown in Supplementary Fig. 22, the operational reliability of the array under repeated\noptical–electrical synaptic cycling is quantitatively evaluated, demonstrating stable and reproducible\nconductance modulation over multiple write–erase sequences, which is essential for large-scale\nneuromorphic deployment.\n(2) Supplementary Information\n[Before revision]\nSupplementary Fig. 11. I of the 12 × 12-pixel EROS array across all 144 pixels.\nSL\n[After revision]\nSupplementary Fig. 17. a, I of the 12 × 12-pixel EROS array across all 144 pixels. b, Box plot\nSL\nshowing the median and distribution of I values under scan line voltage 1 V. c, Individual I value\nSL SL\nunder scan line voltage 1 V across all 144 pixels.\n[Newly added figure]\nSupplementary Fig. 22. Cycle-to-cycle reliability of the EROS array under repeated optical\npotentiation and electrical depression, with statistical evaluation across multiple pixels (number of\npixels = 4).\nComment 6: To highlight the novelty and competitiveness of this work, it is suggested to include a\nperformance comparison table, either in the main text or in the supplementary information,\nbenchmarking the key parameters of this device against those of recently reported devices based on\nother technologies.\nReply\n To directly address the reviewer’s suggestion, we have constructed a new comparison table\nbenchmarking the key performance metrics of our device against recently reported NIR-responsive\nsynaptic transistors based on various material platforms, including metal-oxide, 2D materials, organic\nsemiconductors, and perovskites, as shown in Table R1. Representative works published within the\npast five years were selected for a fair and up-to-date evaluation.\nFor quantitative consistency, we newly calculated the key performance metrics—responsivity and\nenergy consumption—using standardized formulas across all systems as shown in equation (1) and (2).\n𝑃𝑅 =\n𝐽𝑃ℎ𝑜𝑡𝑜\n(1)\n𝑃𝑃ℎ𝑜𝑡𝑜\n𝐸 = 𝑉×𝐼×𝑡 (2)\nwhere 𝐽 is the photocurrent density,𝑃 is the incident laser power density, V is the amplitude of\nphoto photo\nthe programming voltage pulse, I is the current flowing through the device, and t is the programming\npulse width. The responsivity and energy consumption values were extracted from Fig. 2f, g and revised\nSupplementary Fig. 9.\nOur IGZO/UCNP/IGZO device demonstrates responsivities of 116.37 A/W (808 nm) and 57.48 A/W\n(940 nm), and corresponding energy consumptions of 77.7 pJ and 25.03 pJ, respectively. These values\nindicate competitive or superior performance across key metrics.\nThe comparison table has been newly added, and detailed explanations of how responsivity and energy\nconsumption were calculated—including the specific equations used and the figures from which each\nparameter was extracted—are also provided in the supplementary information.\nTable R1. Comparison of key performance metrics of NIR-sensitive synaptic devices\nKey performance\nEnergy\nClass Materials Wavelength Responsivity Pixel Accuracy Ref.\nconsumption\n[nm] [A/W] array [%]\n[J]\nMetal-\nIGZO/Ag2O 830 0.65 - - - 1\noxide\nIGZO/Au NPs 780 3.62 - - 93.8 2\nTa2NiSe5/SnS2 980 14.4 7.88 p - - 3\n2D\nWSe2/InAs 1060 1.134 33.5 p - 96.55 4\nSEBS/\nOrganic 808 1.73 - 10×10 - 5\nPDBT-co-TT\nPEDOT:PSS/\nPerovskite 785 - 3.84 p - 90.53 6\nFaSnI3\n116.37 77.7 p 84.81\nMetal- (808 nm) (808 nm) (808 nm) This\nIGZO/UCNP/IGZO 808, 940 12×12\noxide 57.48 25.03 90.71 work\n(940 nm) (940 nm) (940 nm)\nReferences\nTransistor with Large Conduction States Over 512. Adv. Funct. Mater. 34, 2402222 (2024).\nplasmonic nanoparticles. Nat. Commun. 13, 5081 (2021).\nTa2NiSe5/SnS2 heterojunction from ultraviolet to near-infrared. Light-Sci. Appl. 14, 122\n(2025).\nContaining Multi‐Dimensional van der Waals Heterojunctions. Small 21, 2410892 (2025).\nPhototransistor Arrays. ACS Appl. Mater. Interfaces 16, 36678-36687 (2024).\nHeterojunctions for Neuromorphic Photosensors. Adv. Funct. Mater. 34, 2315175 (2024).\nRevisions in the manuscript\n(1) Page 11\n[Newly added text]\nA performance comparison with recently reported NIR synaptic devices (Supplementary Table 1)\nconfirms that Evo-SynT outperforms existing technologies in terms of responsivity, energy efficiency,\nand recognition accuracy.\n(2) Supplementary Information\n[Newly added table]\nSupplementary Table 1. Comparison of key performance metrics of NIR-sensitive synaptic devices.\nKey performance\nEnergy\nClass Materials Wavelength Responsivity Pixel Accuracy Ref.\nconsumption\n[nm] array\n[A/W] [%]\n[J]\nMetal-\nIGZO/Ag2O 830 0.65 - - - 1\noxide\nIGZO/Au NPs 780 3.62 - - 93.8 2\nTa2NiSe5/SnS2 980 14.4 7.88 p - - 3\n2D\nWSe2/InAs 1060 1.134 33.5 p - 96.55 4\nSEBS/\nOrganic 808 1.73 - 10×10 - 5\nPDBT-co-TT\nPEDOT:PSS/\nPerovskite 785 - 3.84 p - 90.53 6\nFaSnI3\n116.37 77.7 p 84.81\nMetal- (808 nm) (808 nm) (808 nm) This\nIGZO/UCNP/IGZO 808, 940 12×12\noxide 57.48 25.03 90.71 work\n(940 nm) (940 nm) (940 nm)\n[Newly added text]\nResponsivity values were calculated using the equation (1),\n𝑃𝑅 =\n𝐽𝑃ℎ𝑜𝑡𝑜\n(1)\n𝑃𝑃ℎ𝑜𝑡𝑜\nwhere 𝐽 is the photocurrent density and 𝑃 is the incident NIR power density. Photocurrent values\nphoto photo\nwere extracted from experimental transfer characteristics under 808 nm and 940 nm illumination\n(Fig. 2f, g).\nEnergy consumption was estimated using the equation (2),\n𝐸 = 𝑉×𝐼×𝑡 (2)\nwhere 𝑉 is the applied bias (1 V), 𝐼 is the peak postsynaptic current during stimulation, and 𝑡 is the\nstimulation duration (0.5 s). Peak current values were obtained from transient synaptic response plots\n(Supplementary Fig. 9).\nSupplementary references\nTransistor with Large Conduction States Over 512. Adv. Funct. Mater. 34, 2402222 (2024).\nplasmonic nanoparticles. Nat. Commun. 13, 5081 (2021).\nTa2NiSe5/SnS2 heterojunction from ultraviolet to near-infrared. Light-Sci. Appl. 14, 122 (2025).\nContaining Multi‐Dimensional van der Waals Heterojunctions. Small 21, 2410892 (2025).\nPhototransistor Arrays. ACS Appl. Mater. Interfaces 16, 36678-36687 (2024).\nHeterojunctions for Neuromorphic Photosensors. Adv. Funct. Mater. 34, 2315175 (2024).\nComment 7: In the frequency-dependent experiments presented in Figs. 3b and 3c, the total number of\npulses applied varies with frequency. This makes it difficult to attribute the observed differences in\nexcitatory postsynaptic current solely to frequency effects, as they may also arise from differences in the\ncumulative number of pulses.\nReply\n We appreciate the reviewer’s concern. We respectfully clarify that using a fixed stimulation window\nwhile varying pulse frequency is a well-established approach in neuromorphic synapse research and is\nbiologically relevant. In real neurons, higher firing rates within a given time lead to greater postsynaptic\nresponses due to temporal summation – successive EPSCs overlap when inter-spike intervals are short,\nso the responses accumulate instead of fully resetting between spikes. Our stimulation protocol was\ndesigned to capture this frequency-dependent facilitation rather than simply deliver more total pulses.\nIn other words, keeping the time constant isolates how pulse timing (rate) affects synaptic output, which\nis a hallmark of biological synapses (frequent spikes elevate the baseline and produce larger subsequent\ncurrents before the synapse can return to baseline).\nMultiple studies have employed similar protocols to demonstrate frequency-dependent synaptic\nplasticity in hardware devices.1-4 A similar methodology was adopted by Mei et al., where they\ndemonstrated frequency-dependent synaptic plasticity in an organic optoelectronic synapse by applying\nlight pulse trains over fixed durations. Specifically, they delivered spike trains with different pulse\nintervals—3 s, 4 s, and 7 s—each within the same total stimulation time window, thereby modulating\nthe input frequency. The resulting EPSC amplitudes increased with higher stimulation frequency,\nattributed to the temporal overlap and accumulation of individual responses. This protocol closely\nmirrors biological mechanisms, where firing rate within a given period governs synaptic strength. Their\napproach reinforces that using a fixed time window with varying frequency is a biologically relevant\nand experimentally justified method to evaluate frequency-dependent plasticity in neuromorphic\ndevices.1\nLikewise, Xu et al. reported that in a multiterminal synaptic transistor, keeping the stimulation period\nconstant while increasing spike frequency led to much greater potentiation. In their device, pike trains\nof varying frequencies (e.g., 0.5 Hz to 10 Hz) were applied over the same 20-second duration, and the\nresulting EPSC showed a dramatic increase with frequency—for instance, higher-frequency stimulation\n(10 Hz) led to a significantly larger synaptic current compared to low-frequency input (0.5 Hz)—\nattributed to the cumulative overlap of EPSCs within the fixed time window, effectively mimicking\nbiological facilitation.2\nOur own data reflects a similar trend: at higher input frequencies, the EPSC does not fully decay\nbetween pulses, resulting in an elevated baseline and larger successive responses. This behavior is\nconsistent with previously reported observations and suggests that our device captures frequency-\ndependent summation effects. While increasing frequency within a fixed time window naturally leads\nto a greater number of pulses, this protocol is arguably more faithful to biological synaptic behavior\nthan fixing pulse count—since real neurons typically modulate synaptic strength through changes in\nfiring rate over time. Accordingly, we believe our approach provides a biologically relevant and\nexperimentally valid means of evaluating frequency-dependent plasticity in neuromorphic systems.\nReferences\ndoping. Nat. Photonics 17, 629 (2023).\nsynaptic transistor. npj 2D Mater. Appl. 5, 23 (2021).\nand recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic\nsensitization island. Int. J. Extreme Manuf. 7, 06554 (2025).\nefficient neuromorphic computing. InfoMat 3, 904-916 (2021).\nComment 8: The description of the electrical testing method for the EROS array is not sufficiently clear.\nThe authors mention that a patterned shadow mask was used for the learning–forgetting–relearning\ntest. How, specifically, was the conductance of the array measured? A more detailed explanation is\nneeded.\nReply\n We thank the reviewer for pointing out the need for more clarity regarding the electrical testing\nprocedure for the EROS array. The array was measured using a passive matrix configuration, in which\nindividual device conductance was extracted through line-by-line addressing of the crossbar terminals.\nThis approach allowed us to resolve and monitor the conductance evolution of each pixel during\nlearning–forgetting–1st relearning-forgetting-2nd relearning processes. In response, the main manuscript\nhas been updated to include a description of how the array conductance was measured.\nAdditionally, the stimulation parameters for the optical learning phases were not described in\nsufficient detail in the original manuscript. While this information was originally embedded within the\nrevised Supplementary Figs. 18b and 20b (NIR stimulation applied at 1 Hz pulse frequency and a\npower density of 13 mW/mm2 under scan line voltage 1 V), we updated the main manuscript for detailed\noptical stimulation conditions, ensuring that the full testing methodology is now clearly traceable.\nRevisions in the manuscript\n(1) Page 12\n[Newly added text]\nThe array was measured using a passive matrix configuration, where conductance changes in individual\npixels were read out via line-by-line electrical addressing under scan line voltage 1 V. During each\nlearning and relearning phases, the optical stimulation was applied at a pulse frequency of 1 Hz with a\npower density of 13 mW/mm2.\nReviewer #2 Comments:\nThis manuscript proposes a retinocortical in-sensor neuromorphic vision platform for NIR-augmented\nartificial vision. The work is somewhat intriguing and could be considered for potential publication in\nNature Communications after appropriate revisions.\n1. Note that the NIR light power densities range from 13 to 1325 mW/mm2. The light irradiation\ngenerates a significant amount of heat that seriously affects the device performance. The authors should\ninclude a detailed discussion on the impact of such heat.\n2. No evidence is provided to support the assertion that the data in Fig. 3i,k indicate the device’s LTP\nbehavior. Retention measurements of different states during the optical potentiation and electrical\ndepression should be conducted, as reported in Ref. [R1]. Additionally, the device current decays over\ntime for both LTP and LTD, indicating that the current data are not constant. This raises the question\nof how the current data in Fig. 3i,k were obtained. To the best of my knowledge, to date, only Ref. [R1]\naddresses this issue (see the caption of Fig. 4: “The memconductance values were measured 1 s after\neach light pulse” and “Top panel: The device was initially set to a relatively low memconductance of\nabout 100 nS, and then ten SET operations were performed with different numbers of 420 nm light\npulses. After each SET operation and subsequent retention measurement, the device was set to 100 nS\nagain. Bottom panel: The device was initially set to a relatively high memconductance of about 400 nS,\nand then ten RESET operations were conducted with different numbers of 800 nm light pulses. After\neach RESET operation and subsequent retention measurement, the device was set to 400 nS again.”)\nTherefore, to enhance the scientific rigor of the manuscript, the authors are advised to include a detailed\ndescription of the method used to obtain the current data, as outlined in the aforementioned literature.\n3. PPC plays a crucial role in mimicking synaptic behaviors. The authors should include a\ncomprehensive discussion on the device’s PPC mechanism. To enhance understanding of the PPC\nmechanism in oxides, the authors are encouraged to refer to Ref. [R2].\n[R1] Adv. Funct. Mater. 2021, 31, 2005582.\n[R2] Nat. Mater. 2012, 11, 301-305.\nResponse to the reviewer #2 comments\nWe sincerely appreciate the reviewer’s positive assessment and are delighted to hear that the work is\nconsidered intriguing and potentially suitable for publication in Nature Communications. We are\nencouraged by this constructive feedback and have carefully revised the manuscript to further\nstrengthen its technical clarity, mechanistic depth, and overall presentation. We believe that the revised\nversion more clearly conveys the significance and robustness of the proposed retinocortical in-sensor\nneuromorphic vision platform.\nComment 1: Note that the NIR light power densities range from 13 to 1325 mW/mm2. The light\nirradiation generates a significant amount of heat that seriously affects the device performance. The\nauthors should include a detailed discussion on the impact of such heat.\nReply\n We thank the reviewer for raising the important point regarding the potential thermal impact of NIR\nillumination during device operation. To directly evaluate this effect, we newly conducted in-situ\ntemperature measurements using a specialized optical heating analysis setup at the Korea Basic Science\nInstitute (KBSI). This system enables simultaneous NIR irradiation and thermal imaging with a\nvertically aligned thermal camera facing the device substrate.\nThe test was carried out under the most similarly thermally demanding condition used in our study—\n808 nm illumination at a power density of 1 W/mm2 for 10,000 s. Two representative device\nconfigurations were examined: (1) p+-Si/SiO /IGZO, corresponding to the Evo-SynT stack with\n2\nmaximum optical absorption, and (2) glass/SiO /IGZO, reflecting the EROS device structure. As shown\n2\nin Fig. R9, the substrate temperature for the p+-Si sample increased from 24.35 OC to 47.77 OC after\n10,000 s of continuous exposure. The typical operation timescale for our device measurements—such\nas synaptic weight modulation or photoresponse acquisition—was on the order of 500 s. At this duration,\nthe observed temperature rise was modest, reaching only 38.91 OC, which is near ambient conditions.\nSimilarly, as shown in Fig. R10, the glass-based sample exhibited a temperature rise from 25.45 OC to\nonly 30.40 OC under the same conditions—indicating a modest 4.95 OC increase, which remains close\nto ambient. Importantly, such temperatures are well below the thermal stability limits of IGZO-based\ndevices, which are known to operate reliably up to 80–100 °C without significant degradation.1,2\nThese results indicate that even under the most thermally demanding illumination conditions employed\nin this study, the substrate temperature remained within a range that is unlikely to influence device\nbehavior. Specifically, the maximum temperature reached under prolonged exposure was 47.77 °C for\nthe Evo-SynT configuration and 30.40 °C for the EROS-compatible structure. We conclude that thermal\neffects did not contribute significantly to the observed device performance. This analysis has been\nincorporated into the revised manuscript to clarify that the reported optoelectronic responses were not\nthermally induced.\nFig. R9. Thermal evaluation of the p+-Si/SiO /IGZO under prolonged NIR illumination. a, Time-\n2\ndependent temperature profile of a p+-Si/SiO /IGZO substrate measured under 808 nm NIR light at\n2\nFig. R10. Thermal evaluation of the glass/SiO /IGZO under prolonged NIR illumination. a, Time-\n2\ndependent temperature profile of a glass/SiO /IGZO substrate measured under 808 nm NIR light at\n2\nReference\nfilm transistors for High-Mobility and Low-Power Oxide Electronics. Commun. Mater. 6, 280\n(2025).\noptimizing composition ratio. J. Alloy. Compd. 1022, 179753 (2025).\nRevisions in the manuscript\n(1) Page 7\n[Newly added text]\nTo further ensure that the observed photocurrent does not originate from thermal effects but exclusively\nfrom the upconversion process, we additionally conducted in-situ temperature measurements under\nprolonged 808 nm illumination at a power density of 1 W/mm2 for 10,000 s. The p+-Si/SiO /IGZO was\n2\ntested. The sample exhibited a temperature increase from 24.35 oC to 47.77 oC, as shown in\nSupplementary Fig. 8. Considering that typical measurement durations are on the order of 10 s, during\nwhich the temperature remained close to room temperature, these results confirm that the photoresponse\nis not thermally driven but is a direct consequence of UCNP-mediated upconversion.\n(2) Supporting Information\n[Newly added figure]\nSupplementary Fig. 8. Time-dependent temperature profile of a p+-Si/SiO /IGZO substrate measured\n2\nunder 808 nm NIR light at 1 W/mm2 for 10,000 s.\nComment 2: No evidence is provided to support the assertion that the data in Fig. 3i,k indicate the\ndevice’s LTP behavior. Retention measurements of different states during the optical potentiation and\nelectrical depression should be conducted, as reported in Ref. [R1]. Additionally, the device current\ndecays over time for both LTP and LTD, indicating that the current data are not constant. This raises\nthe question of how the current data in Fig. 3i,k were obtained. To the best of my knowledge, to date,\nonly Ref. [R1] addresses this issue (see the caption of Fig. 4: “The memconductance values were\nmeasured 1 s after each light pulse” and “Top panel: The device was initially set to a relatively low\nmemconductance of about 100 nS, and then ten SET operations were performed with different numbers\nof 420 nm light pulses. After each SET operation and subsequent retention measurement, the device\nwas set to 100 nS again. Bottom panel: The device was initially set to a relatively high memconductance\nof about 400 nS, and then ten RESET operations were conducted with different numbers of 800 nm light\npulses. After each RESET operation and subsequent retention measurement, the device was set to 400\nnS again.”) Therefore, to enhance the scientific rigor of the manuscript, the authors are advised to\ninclude a detailed description of the method used to obtain the current data, as outlined in the\naforementioned literature.\nReply\n Retention measurements for different synaptic states during both optical potentiation and electrical\ndepression were newly conducted, following a protocol directly comparable to Ref. [R1], and are now\nprovided in Fig. R11 and R12.\nFor optical potentiation, distinct synaptic states were programmed by applying a controlled number of\nNIR light pulses (808 nm or 940 nm) with identical pulse conditions (power density: 13 mW/mm2 ,\nfrequency: 1 Hz), as shown in Fig. R11. To ensure that the extracted PSC values represent steady\nsynaptic weights, we defined the readout point as 10 s after the final optical pulse. This deliberate delay\nsuppresses fast photocarrier relaxation and excludes short-lived components immediately following\nillumination. After this readout, the PSC was continuously monitored for 100 s under the same electrical\nbias to evaluate the retention behavior of each programmed LTP level.\nBecause gradual current decay is intrinsic to oxide-based photonic synapses, the retention traces were\nfurther analyzed by linear fitting (y = a + b·t) to quantify state stability over time. Importantly, we\nassessed multilevel synaptic retention using a state-separability criterion, where each programmed level\nis considered retained as long as it remains distinguishable from neighboring levels (defined by half of\nthe inter-level PSC spacing). Under this rigorous criterion, the optically programmed states remain\nseparable for 57 s (64 pulses) to 795 s (1 pulse) at 808 nm and 68 s (64 pulses) to 803 s (1 pulse) at 940\nnm. These results confirm that the LTP levels are not transient artifacts but represent physically stable,\ndistinguishable synaptic states that can be maintained beyond the 100 s measurement window,\nsupporting hour-scale stability trends when extrapolated from the fitted decay slopes.\nFig. R11. Retention and state separability of multilevel optical potentiation states in Evo-SynT at a,\n808 nm and b, 940 nm.\nFor electrical depression, states were evaluated in the same rigorous manner as optical LTP, as shown\nin Fig. R12. After programming the device into a highly potentiated state using 200 optical pulses (808\nnm or 940 nm, 13 mW/mm2, 1 Hz), electrical depression was induced by applying sequences of gate\nvoltage pulses (either 1.5 V or 1.0 V) with increasing pulse numbers. The PSC was read out 10 s after\nthe final electrical pulse to suppress fast capacitive and transient charging effects and to ensure that the\nextracted current represents a steady synaptic weight.\nThe PSC of each electrically programmed depression level was then continuously monitored for 100\ns under the same bias conditions to evaluate retention. The retention traces were also analyzed by linear\nfitting using y = a + b·t. For 1.5 V electrical depression, five distinct LTD states were programmed\nusing increasing numbers of voltage pulses. Using the same state-separability criterion as for optical\npotentiation, the electrically programmed states remain distinguishable for 70 s (64 pulses) up to 600 s\n(1 pulse), confirming stable multilevel depression behavior over extended time scales. Similarly, for\n1.0 V depression, the decay slopes are smaller, resulting in slower relaxation. Accordingly, the LTD\nstates remain separable for 120 s (64 pulses) up to 900 s (1 pulse), indicating even stronger retention at\nthe lower depression voltage.\nFig. R12. Retention and state separability of multilevel electrical depression states in Evo-SynT at a,\n1.5 V and b, 1 V.\nIn line with the reviewer’s suggestion, the full measurement protocol has now been explicitly added to\nthe revised manuscript and supplementary information, including the readout delay after optical and\nelectrical pulses (10 s), the retention duration (100 s), and the optical programming conditions (200\npulses, 1 Hz, 13 mW/mm2 at 808 and 940 nm). This ensures a level of methodological rigor directly\naligned with Ref. [R1] while extending it to NIR-driven optoelectronic synapses.\nRevisions in the manuscript\n(1) Page 9\n[Newly added text]\nFor the synaptic weight curves shown in Fig. 3i and 3k, both optical potentiation and electrical\ndepression were implemented using pulse trains at 1 Hz. Optical pulses (808 or 940 nm, 13 mW/mm2)\nwere applied to program LTP, and electrical gate pulses were used to induce long-term depression\n(LTD). In all cases, the PSC was read out 0.5 s after each pulse to suppress fast transient and capacitive\ncomponents and to ensure that the extracted values represent quasi-steady synaptic weights, following\nthe protocol commonly used in photonic synapse studies.\n(2) Page 10\n[Newly added text]\nTo further verify that these multilevel synaptic states are not transient artifacts but represent physically\nstable states, retention measurements were conducted for both optically potentiated and electrically\ndepressed levels, as shown in Supplementary Fig. 12 and 13.54 Distinct synaptic states were first\nprogrammed using defined numbers of optical or electrical pulses, and the postsynaptic current was\nread 10 s after the final pulse to suppress fast transient components. The PSC was then monitored for\n100 s and analyzed by linear fitting to evaluate state stability. Using a state-separability criterion based\non inter-level spacing, the optically programmed LTP states remained distinguishable for 57 s (64\npulses) up to 795 s (1 pulse) at 808 nm and 68 s (64 pulses) up to 803 s (1 pulse) at 940 nm. For electrical\ndepression, the LTD states remained separable for 70 s (64 pulses) up to 600 s (1 pulse) at 1.5 V and\n120 s (64 pulses) up to 900 s (1 pulse) at 1.0 V, confirming stable multilevel electrical erasure with\nvoltage-dependent retention strength.\n(3) Reference\n[Newly added reference]\nComputing. Adv. Funct. Mater. 31, 2005582 (2020).\n(4) Supplementary Information\n[Newly added figures]\nSupplementary Fig. 12. Retention and state separability of multilevel optical potentiation states in\nEvo-SynT at a, 808 nm and b, 940 nm. (Optical potentiation condition: 1 Hz frequency, 13 mW/mm2\npower density)\nSupplementary Fig. 13. Retention and state separability of multilevel electrical depression states in\nEvo-SynT at a, 1.5 V and b, 1 V. (After 200 optical pulses (808 nm or 940 nm, 13 mW/mm2, 1 Hz),\nelectrical depression condition: 1 Hz frequency)\nComment 3: PPC plays a crucial role in mimicking synaptic behaviors. The authors should include a\ncomprehensive discussion on the device’s PPC mechanism. To enhance understanding of the PPC\nmechanism in oxides, the authors are encouraged to refer to Ref. [R2].\nReply\n We appreciate the reviewer’s suggestion to strengthen the discussion of the PPC mechanism and for\npointing us to Ref. [R2], which provides valuable insight into oxygen-vacancy-mediated PPC in oxide\nsemiconductors. As noted, our manuscript already describes this mechanism in detail—specifically, the\nphotoionization of deep-level neutral oxygen vacancies (V ) into donor-like V + and V 2+ states, which\nO O O\nrelease free electrons and contribute to the persistent photocurrent (see main text page 6~7 and revised\nSupplementary Fig. 6).\nNonetheless, to further reinforce the link to prior work, we have revised the manuscript to explicitly\ncite Ref. [R2] at the end of the relevant paragraph. In addition, we have updated the wording to adopt\nthe terminology used in Ref. [R2]—specifically, distinguishing between deep-level neutral oxygen\nvacancies (V ) and their ionized states (V + and V 2+)—to improve clarity and consistency. This\nO O O\nrevision provides a more comprehensive discussion of the PPC mechanism by aligning our\ninterpretation with the well-established model of photoinduced vacancy ionization and metastable trap\ndynamics described in oxide semiconductors.\nRevisions in the manuscript\n(1) Page 6~7\n[Before revision]\nThe UV and blue photons generated by UCNPs possess sufficient energy to excite trap states associated\nwith oxygen vacancies (V ) within the IGZO channel.\nO\n[After revision]\nThe UV and blue photons generated by UCNPs possess sufficient energy to excite trap states associated\nwith deep-level neutral oxygen vacancies (V ) within the IGZO channel.\nO\n[Newly added text]\nThis PPC mechanism, involving V ionization and slow recombination kinetics, is consistent with prior\nO\nobservations in IGZO, further validating the oxygen vacancy origin of PPC in Evo-SynT.51\n(2) Reference\n[Newly added reference]\nphotoconductivity in oxide semiconductor photosensor arrays. Nat. Mater. 11, 301 (2012).\nReviewer #3 Comments:\nThis paper presents an exciting study that combines IGZO transistors with NaYF4:Yb/Tm upconversion\nnanoparticles to realize an in-sensor neuromorphic vision platform capable of sensing up to the near-\ninfrared (NIR) range. The dual-mode (preprocessing-postprocessing) concept, which mimics the human\nretina-cortex architecture, is innovative, and the hardware-based demonstration of night vision\ndemonstrates its potential applications. However, further verification is required for the actual\nphotoconversion efficiency of the UCNP-IGZO combination, the identification of the fundamental\nmechanisms of synaptic activity, and the quantitative reproducibility of the YOLO simulation. Overall,\nthe idea and system integration are excellent, but further experimental evidence and comparative\nanalysis of the data will be necessary to fully support its publication value.\n1. It is unclear how effectively the upconversion efficiency of NaYF4:Yb/Tm nanoparticles (especially\nwhen operated at 808 nm and 940 nm) couples with the actual absorption band of IGZO. A quantitative\nrelationship between the absorption rate of photons emitted from UCNPs, the internal quantum\nefficiency, and the charge generation efficiency within the IGZO layer needs to be demonstrated\nexperimentally or through simulation. Also physical mechanism should be reinforced.\n2. In structures where UCNP layers are inserted between IGZO layers, thermal and chemical\ndegradation, ion diffusion, and increased interfacial defects are highly likely to occur during prolonged\nlight irradiation or electrical operation. It is necessary to evaluate whether this structural stability is\nmaintained over long-term repeated operation (thousands of cycles or more), and to evaluate current\ndrift, hysteresis, and fatigue phenomena.\n3. Although PPC following NIR irradiation was interpreted as synaptic weight maintenance, it could\nalso be explained by a simple phototransduction (trapping–detrapping) phenomenon. It appears that\nLTP was induced by simple changes in light intensity, without typical pulse-interval-dependent\nplasticity or voltage-dependent modulation. However, it is important to clarify how synaptic\nemulation and simple phototransistor operation were distinguished.\n4. The presented 12 x12 array appears to be a proof-of-concept. System-level limitations, such as optical\ncrosstalk, electrical interference, addressability, and frame rate, that will arise when scaling to higher\nresolutions are not discussed at all. Design solutions or circuit architecture proposals are needed to\naddress these scalability issues.\n5. While my paper highlights the advantages of the IGZO-based platform, a direct comparison of actual\nNIR sensitivity, response speed, SNR, power consumption, and other numerical features with existing\n2D materials, perovskite, and organic phototransistor-based NIR devices was not conducted.\nPerformance benchmarking (responsivity, detectivity, retention time) is necessary to truly evaluate the\ncompetitiveness of the proposed structure.\n6. For image detection simulation work, it is unclear whether the data is obtained from actual hardware\nor is a simple function-fitting-based simulation, and its performance advantage over software has not\nbeen clearly proven.\nResponse to the reviewer #3 comments\nWe sincerely thank the reviewer for the encouraging and insightful comments on the novelty and\nsystem-level integration of our work. We are particularly pleased that the dual-mode retinocortical\narchitecture and the hardware-based night-vision demonstration were recognized as innovative and\nimpactful. In response to the reviewer’s concerns, we have added quantitative photoconversion analysis\nof the UCNP–IGZO interface, significantly expanded the mechanistic explanation of synaptic behavior\nwith additional electrical and optical characterizations, and strengthened the reproducibility and\nstatistical validation of the YOLO-based system-level evaluation. Furthermore, we have included\ncomparative benchmarking with existing in-sensor neuromorphic vision systems to better contextualize\nthe performance of our platform. We believe these revisions provide the necessary experimental rigor\nand clarity to fully support the publication value of this work.\nComment 1: It is unclear how effectively the upconversion efficiency of NaYF4:Yb/Tm nanoparticles\n(especially when operated at 808 nm and 940 nm) couples with the actual absorption band of IGZO. A\nquantitative relationship between the absorption rate of photons emitted from UCNPs, the internal\nquantum efficiency, and the charge generation efficiency within the IGZO layer needs to be\ndemonstrated experimentally or through simulation. Also physical mechanism should be reinforced.\nReply\n We agree that quantifying the coupling between UCNP emission and IGZO absorption, as well as\nciting the upconversion efficiency, is crucial for evaluating real-world performance. Regarding the\nNaYF :Yb/Tm nanoparticles, we clarify that the materials employed in this study are well-established\n4\ncore UCNPs. We acknowledge that such systems typically exhibit an internal upconversion quantum\nyield (UCQY) in the range of approximately 0.5–4%, as reported in previous literature.1-5\nTo quantitatively evaluate the optical coupling between UCNP emission and IGZO absorption, we\nperformed a spectral overlap–based photon absorption analysis. The optical bandgap of the IGZO\nchannel layer was estimated to be 3.21 eV from Tauc analysis (revised Supplementary Fig. 5),\ncorresponding to an absorption onset near 386 nm. Using experimentally measured UCNP emission\nspectra, we calculated the fraction of upconverted photon flux that lies within this effective absorption\nwindow. Specifically, we define the optical coupling efficiency (𝜂 ) as:\ncoupling\n∫Φ (𝜆) 𝐴 (𝜆) 𝑑𝜆\nUCNP IGZO\n𝜂 = ×100 (%)\ncoupling\n∫Φ (𝜆) 𝑑𝜆\nUCNP\nwhere Φ (𝜆) represents the UCNP photon flux spectrum and 𝐴 (𝜆) is the wavelength\nUCNP IGZO\ndependent absorption probability of the IGZO channel. This formulation directly reflects the fraction\nof emitted photons that can be absorbed by the IGZO layer. From this analysis, we obtained an estimated\n𝜂 of approximately 21.6 %, indicating that a finite but non-negligible portion of the upconverted\ncoupling\nemission can be effectively harvested by the IGZO channel.\nTo further connect this optical coupling to the actual electrical output of the device, we converted the\nexperimentally measured responsivity into an effective photon-to-electron conversion factor.\nSpecifically, the number of collected electrons per incident NIR photon is given by:\n𝑁 𝐼/𝑞 ℎ𝑐\n𝜂𝑒𝑥𝑡 = 𝑒 = = 𝑅×\ne/ph 𝑁𝑖𝑛 ℎ𝑐 𝑞𝜆\n𝑝ℎ 𝑃/( )\n𝜆\nwhere 𝑁 is number of electrons collected per second, 𝑁𝑖𝑛 is number of photons incident per second, 𝐼\n𝑒 𝑝ℎ\nis photocurrent, P is light power, and R is the measured responsivity. Using the responsivities of 116.37\nA/W at 808 nm and 57.48 A/W at 940 nm, we obtain 𝜂ext ≈ 1.79×102(808 nm) and ≈ 7.58×\ne/ph\n101(940 nm). These values mean that each incident NIR photon gives rise to tens to hundreds of\ncollected charge carriers in the external circuit, reflecting strong photoconductive gain arising from\ntrap-assisted long carrier lifetimes in IGZO.\nImportantly, this gain mechanism compensates for the intrinsically limited upconversion quantum yield\nof NaYF :Yb/Tm UCNPs. Even though only a fraction of NIR photons is upconverted and only ~21.6%\n4\nof the resulting visible photons are spectrally matched to IGZO absorption, each absorbed upconverted\nphoton can modulate the channel conductance for an extended time, leading to a greatly amplified\nelectrical response. Taken together, the spectral coupling efficiency (𝜂 ), the known UCQY\ncoupling\nrange of the UCNPs, and the large 𝜂ext quantitatively establish a consistent physical pathway from\ne/ph\nNIR excitation → UCNP upconversion → IGZO absorption → charge generation and amplification in\nthe Evo-SynT device.\nThese quantitative metrics and their detailed derivations have been added to the revised manuscript\nand supplementary information to clarify the optical–electrical coupling and charge-generation\nefficiency of the Evo-SynT device.\nReference\nytterbium-sensitized upconversion phosphors using the ytterbium (iii) emission as an internal\nstandard. Nanoscale 10, 17212-17226 (2018).\nnanocrystals. Chem. Soc. Rev. 38, 976-989 (2009).\nMater. 10, 968-973 (2011).\nenhanced stability for efficient photodynamic therapy. J. Nanobiotechnol. 18, 85 (2020).\nRevisions in the manuscript\n(1) Page 6\n[Newly added text]\nTo quantify this energy transfer efficiency, we performed a spectral overlap–based analysis. We defined\nthe optical coupling efficiency (η ) as the ratio of the absorbed photon flux to the total emitted flux,\ncoupling\ncalculated by integrating the overlap between the UCNP emission spectrum and the IGZO spectral\nabsorptance as shown in Supplemetary Eq. 1. This calculation yields an estimated η of\ncoupling\napproximately 21.6%, confirming that the dominant NaYF :Yb/Tm emission peaks (e.g., 290 nm, 345\n4\nnm, 360 nm) are effectively harvested by the active layer. Crucially, while typical NaYF :Yb/Tm\n4\nsystems exhibit a modest internal upconversion quantum yield (UCQY) of 0.5–4%, the high optical\ncoupling combined with the intrinsic photoconductive gain of the transistor compensates for the limited\nphoton flux.32,33,52,53\n(2) Supplementary Information\n[Newly added eqaution]\nSupplementary Eq. 1. Optical Coupling Efficiency.\n∫Φ (𝜆) 𝐴 (𝜆) 𝑑𝜆\nUCNP IGZO\n𝜂 = ×100 (%)\ncoupling\n∫Φ (𝜆) 𝑑𝜆\nUCNP\nwhere Φ (𝜆) represents the UCNP photon flux spectrum and 𝐴 (𝜆) is the wavelength dependent\nUCNP IGZO\nabsorption probability of the IGZO channel.\n(3) References\n[Newly added references]\nytterbium-sensitized upconversion phosphors using the ytterbium (iii) emission as an internal standard.\nNanoscale 10, 17212-17226 (2018).\nenhanced stability for efficient photodynamic therapy. J. Nanobiotechnol. 18, 85 (2020).\nComment 2: In structures where UCNP layers are inserted between IGZO layers, thermal and chemical\ndegradation, ion diffusion, and increased interfacial defects are highly likely to occur during prolonged\nlight irradiation or electrical operation. It is necessary to evaluate whether this structural stability is\nmaintained over long-term repeated operation (thousands of cycles or more), and to evaluate current\ndrift, hysteresis, and fatigue phenomena.\nReply\n We appreciate the reviewer’s insightful concern regarding the long-term structural and operational\nstability of UCNP–IGZO heterostructures under prolonged optical and electrical cycling, particularly\nwith respect to possible thermal effects, interfacial degradation, and fatigue.\nTo first exclude thermal artifacts, we newly conducted in-situ temperature measurements using an\noptical heating analysis system at the Korea Basic Science Institute (KBSI), which enables simultaneous\n808 nm NIR illumination and thermal imaging of the device substrate, as shown in Fig. R13 and R14.\nThe measurements were performed under the most thermally demanding condition employed in this\nstudy (808 nm, 1 W/mm2 for 10,000 s). For the p+-Si/SiO /IGZO stack (Evo-SynT configuration), the\n2\nsubstrate temperature, increased from 24.35 °C to 47.77 °C after 10,000 s, while during the typical\ndevice operation timescale (~500 s) it reached only 38.91 °C. For the glass/SiO /IGZO structure\n2\n(EROS-compatible), the temperature rose from 25.45 °C to only 30.40 °C under the same illumination.\nThese temperatures are far below the thermal stability limit of IGZO-based devices (80–100 °C),\nconfirming that the observed optoelectronic behavior is not thermally induced.1,2\nFig. R13. Thermal evaluation of the p+-Si/SiO /IGZO under prolonged NIR illumination. a, Time-\n2\ndependent temperature profile of a p+-Si/SiO /IGZO substrate measured under 808 nm NIR light at\n2\nFig. R14. Thermal evaluation of the glass/SiO /IGZO under prolonged NIR illumination. a, Time-\n2\ndependent temperature profile of a glass/SiO /IGZO substrate measured under 808 nm NIR light at\n2\nTo further examine long-term structural and electrical stability, we newly performed repeated\noptical–electrical cycles consisting of 250 optical potentiation pulses (808 nm, 13 mW/mm2) and 250\nelectrical depression pulses (1.5 V), repeated three times (1,500 total cycles), as shown in Fig. R15a,\nfollowed by transfer hysteresis measurements. Fig. R15b and R15c compare the transfer characteristics\nbefore and after the 1500 cycle operation. After cycling, both the threshold voltage and hysteresis\nwindow increased by approximately ~2 V, indicating accumulated charge trapping induced by gate-\nfield-assisted injection of photo-generated carriers.1,2 Importantly, however, the subthreshold swing\nremained nearly unchanged and the on/off ratio was well preserved, demonstrating that the IGZO\nchannel and interfacial integrity were maintained without structural degradation. Although a moderate\nincrease in off-current was observed, this behavior is attributed to residual photo-induced carriers and\ntrap-assisted conduction rather than thermal damage or material failure.\nThese results demonstrate that the UCNP-integrated IGZO heterostructure sustains repeated optical–\nelectrical operation over thousands of cycles, with the primary effect being controlled charge-trapping-\nmediated consolidation of synaptic states rather than degradation of device performance. The\ncorresponding temperature analysis and long-term stability data have been incorporated into the revised\nmanuscript and supplementary information.\nFig. R15. Long-term stability of Evo-SynT under repeated optical–electrical learning cycles. a,\nRepeated optical–electrical cycles consisting of 250 optical potentiation pulses (808 nm, 13 mW/mm2)\nand 250 electrical depression pulses (1.5 V), repeated three times (1,500 total cycles). Transfer\nhysteresis b, before long-term cycling and c, after 1500 optical–electrical cycles\nReference\nfilm transistors for High-Mobility and Low-Power Oxide Electronics. Commun. Mater. 6, 280\n(2025).\noptimizing composition ratio. J. Alloy. Compd. 1022, 179753 (2025).\nRevisions in the manuscript\n(1) Page 7\n[Newly added text]\nTo further ensure that the observed photocurrent does not originate from thermal effects but exclusively\nfrom the upconversion process, we additionally conducted in-situ temperature measurements under\nprolonged 808 nm illumination at a power density of 1 W/mm2 for 10,000 s. The p+-Si/SiO /IGZO was\n2\ntested. The sample exhibited a temperature increase from 24.35 oC to 47.77 oC, as shown in\nSupplementary Fig. 8. Considering that typical measurement durations are on the order of 10 s, during\nwhich the temperature remained close to room temperature, these results confirm that the photoresponse\nis not thermally driven but is a direct consequence of UCNP-mediated upconversion.\n(2) Page 10~11\n[Newly added text]\nTo evaluate the endurance of the programmed synaptic states under realistic learning conditions,\nprolonged optical–electrical cycling tests were performed. The Evo-SynT was repeatedly trained using\nalternating optical potentiation and electrical depression for a total of 1500 cycles, mimicking extended\nlearning and forgetting processes, as shown in Supplementary Fig. 14. Throughout this endurance test,\nthe devices maintained stable and controllable postsynaptic current modulation without abrupt\ndegradation or loss of switching functionality, demonstrating that the UCNP-integrated heterostructure\ncan withstand repeated photo-electrical stress. This result confirms that the multilevel synaptic states\nare not only retained over time but also preserved under long-term repeated operation, supporting the\nreliability of the Evo-SynT platform for neuromorphic optoelectronic systems.\n(3) Supporting Information\n[Newly added figure]\nSupplementary Fig. 8. Time-dependent temperature profile of a p+-Si/SiO /IGZO substrate measured\n2\nunder 808 nm NIR light at 1 W/mm2 for 10,000 s.\nSupplementary Fig. 14. Long-term stability of Evo-SynT under repeated optical–electrical learning\ncycles. Repeated optical–electrical cycles consisting of 250 optical potentiation pulses (808 nm, 13\nmW/mm2) and 250 electrical depression pulses (1.5 V), repeated three times (1,500 total cycles).\nComment 3: Although PPC following NIR irradiation was interpreted as synaptic weight maintenance,\nit could also be explained by a simple phototransduction (trapping–detrapping) phenomenon. It\nappears that LTP was induced by simple changes in light intensity, without typical pulse-interval-\ndependent plasticity or voltage-dependent modulation. However, it is important to clarify how synaptic\nemulation and simple phototransistor operation were distinguished.\nReply\n The observed LTP and PPC behaviors in our device are not solely the result of simple phototransistor\ndynamics. Rather than being induced by mere changes in light intensity, the LTP characteristics emerge\nfrom the application of temporally structured pulse trains with defined frequency, pulse number, and\ninter-pulse interval—protocols designed to emulate biologically inspired synaptic stimulation. As\nshown in Fig. 3b–g and revised Supplementary Fig. 9, the synaptic weight enhancement exhibits clear\ndependencies on pulse frequency and count, aligning with cumulative learning behaviors commonly\nobserved in neural systems. These trends differ from the immediate and largely reversible photocurrent\nresponses typically associated with conventional phototransistors.\nFurthermore, the PPC observed in our device is not explained by shallow trap-based trapping–\ndetrapping dynamics. As discussed in the main text (page 6~7) and illustrated in revised\nSupplementary Fig. 6, the PPC arises from the ionization of deep-level neutral oxygen vacancies (V )\nO\nwithin IGZO, which release electrons and form metastable donor states (V + and V 2+) upon absorption\nO O\nof blue and UV photons upconverted from NIR-excited UCNPs. These ionized states exhibit long\nlifetimes due to kinetic barriers to recombination, leading to slow decay and enabling analog retention\nof optical memory—an essential feature of neuromorphic behavior.1\nWhile phototransistors typically respond to light via simple, monotonic photocurrent generation with\nlimited temporal sensitivity, our Evo-SynT exhibits hallmark features of synaptic function that cannot\nbe explained by phototransduction alone:\n1) Paired-pulse facilitation (PPF): As shown in Fig. 3a, our device exhibits a clear\nexponential decay in the PPF index with increasing inter-spike interval (Δt), which closely\nmirrors biological short-term plasticity. Such Δt-dependent facilitation is not observed in\nconventional phototransistor operation, where photocurrent is independent of pulse timing.\n2) Cumulative transition from STP to LTP: Fig. 3b–g and revised Supplementary Fig. 9\ndemonstrate that repeated optical stimulation leads to a gradual and nonlinear increase in\npostsynaptic current (PSC), consistent with a short-term to long-term potentiation\ntransition. This behavior reflects cumulative memory formation, which does not arise from\nindividual trap dynamics in basic phototransistors.\n3) Bidirectional, analog weight modulation: Fig. 3h–k illustrate that the Evo-SynT enables\nmulti-level potentiation via NIR light and reversible depression via electrical pulses,\nallowing symmetric and analog tuning across 512 conductance states. Such precise\nbidirectional control is beyond the capability of typical phototransistors, which lack\nelectrical reversibility or multilevel weight states.\nCollectively, these results confirm that the Evo-SynT operates beyond the scope of simple\nphototransistor behavior and functions as a neuromorphic device with biologically relevant plasticity\nmechanisms. To emphasize this distinction, we have now revised the manuscript text accordingly to\nhighlight the spatiotemporal and bidirectional control features that underpin true synaptic emulation.\nReference\nphotoconductivity in oxide semiconductor photosensor arrays. Nat. Mater. 11, 301 (2012).\nRevisions in the manuscript\n(1) Page 11\n[Newly added text]\nThese results collectively demonstrate that the Evo-SynT reliably emulates key synaptic behaviors—\nincluding STP and LTP, linear and reversible weight modulation, and frequency-dependent learning—\nunder biologically inspired stimulation protocols. Unlike conventional phototransistors, which typically\nexhibit monotonic and intensity-dependent responses, Evo-SynT shows sensitivity to the temporal\ndynamics of optical pulses such as frequency, interval, and pulse count. This temporal dependence\nreflects neuromorphic functionality and distinguishes the Evo-SynT from simple phototransduction-\nbased devices.\nComment 4: The presented 12 x12 array appears to be a proof-of-concept. System-level limitations,\nsuch as optical crosstalk, electrical interference, addressability, and frame rate, that will arise when\nscaling to higher resolutions are not discussed at all. Design solutions or circuit architecture proposals\nare needed to address these scalability issues.\nReply\n We appreciate the reviewer’s insightful comment regarding the scalability of the 12 × 12 EROS\narray. While large-scale integration was not the primary focus of the present study, we fully agree that\na discussion of system-level limitations and architectural considerations is essential to evaluate the\nfeasibility of implementing the proposed retinocortical vision platform in real-world applications.\nTo address this point, we have added a dedicated paragraph to the Discussion section outlining how\nthe key scalability concerns—optical crosstalk, electrical interference, addressability, and frame rate—\ncan be mitigated or accommodated by design. Our summary is as follows:\n1) Optical crosstalk: The vertically stacked IGZO/UCNP/IGZO structure inherently confines\nlight absorption to the subpixel region. In addition, the surrounding metal interconnects\nact as lateral optical shields. For higher-resolution implementations, further measures such\nas patterned optical shielding layers1, or microlens arrays2 could be adopted to suppress\nlateral photon leakage between adjacent pixels.\n2) Electrical interference: Each pixel contains a dedicated switching transistor that provides\nper-pixel isolation during readout. Standard mitigation techniques used in active-matrix\narchitectures, such as ground shielding3, and differential sensing4, can be implemented to\nreduce parasitic coupling and ensure signal integrity across large arrays.\n3) Addressability: The current pixel design follows a passive-matrix compatible row/column\nscheme, which can be directly adapted into an active-matrix architecture using well-\nestablished TFT driver circuitry.5 This ensures that the addressing protocol scales linearly\nwith resolution, as in conventional flat-panel displays and imagers.\n4) Fram rate: The achievable frame rate is determined by the array readout speed and the\nintrinsic time constants of the EROS array. Importantly, the device’s plasticity timescale\ncan be tuned by adjusting optical or electrical stimulation parameters, allowing the\nplatform to be configured for either high-speed sensing (e.g., real-time image capture) or\nslow-memory operation (e.g., adaptive vision), depending on application needs.6\nThese system-level design considerations have now been incorporated into the revised supplementary\ninformation to clarify that the current implementation serves as a scalable foundation. The Evo-SynT\npixel architecture is inherently compatible with high-density integration, and the EROS platform can\nbe extended toward larger formats through standard circuit and packaging techniques. This update\ndirectly addresses the reviewer’s concerns by providing a practical roadmap for scaling the device\ntoward functional neuromorphic vision systems.\nReference\ncolour-converted micro-LED display with a 122% NTSC gamut. Light-Adv. Manuf. 3, 36\n(2022).\nImage Sensors. Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging Sensors and Systems.\n93-1 (2021).\nSingle-Ended Cyclic ADCs. IEEE Trans. Electron Devices 11, 2414-2422 (2009).\nmatrix flat-panel imagers using compensation lines. Med. Phys. 8, 1855-1864 (2000).\nMaterials for Application: From Lab to Industry. Electronics 13, 241 (2024).\n15, 3257 (2024).\nRevisions in the manuscript\n(1) Page 13\n[Newly added text]\nWhile the current array demonstrates proof-of-concept functionality, its scalability toward high-\nresolution neuromorphic systems is further addressed in Supplementary Discussion 1, including\ndesign strategies to mitigate optical and electrical crosstalk, ensure addressability, and support flexible\nframe-rate operation.\n(2) Supplementary information\n[Newly added discussion]\nSupplementary Discussion 1. System-level scalability considerations for the EROS array.\nTo assess the scalability of the 12 × 12 EROS array toward high-resolution neuromorphic vision\napplications, we outline system-level challenges and feasible architectural solutions for future\nintegration. The following discussion addresses four key aspects, optical crosstalk, electrical\ninterference, addressability, and frame rate, and highlights how each can be systematically mitigated or\nleveraged through circuit and pixel-level design enhancements.\n1. Optical crosstalk\nThe vertically stacked IGZO/UCNP/IGZO architecture inherently confines photoactivation to the\nvertical subpixel channel, reducing out-of-plane scattering. Additionally, surrounding metal\ninterconnects form lateral optical shields that suppress in-plane light leakage. For larger arrays,\nadditional methods such as patterned optical shielding layers, microlens arrays, and integrated spectral\nfilters can be implemented to further enhance spatial confinement and minimize crosstalk between\nadjacent pixels. These approaches have been validated in advanced image sensors and neuromorphic\nphotonic circuits for pixel-level isolation and spectral selectivity.1,2\n2. Electrical Interference\nTo ensure electrical integrity in large-scale arrays, each EROS pixel includes a dedicated switching\ntransistor, which provides per-pixel readout isolation and reduces cross-talk between rows and columns.\nAt the interconnect level, conventional mitigation techniques from active-matrix architectures—such\nas ground-plane shielding, differential signaling, and column-parallel amplification—can be adopted to\nsuppress capacitive coupling and noise propagation across the array.3,4 These strategies are widely used\nin commercial active-matrix OLED and image sensor backplanes, underscoring their scalability and\ncompatibility with the Evo-SynT architecture.\n3. Addressability\nThe current array follows a row/column passive-matrix addressing scheme, which is inherently scalable\nto active-matrix configurations using standard TFT driver circuitry. This architectural compatibility\nenables linear scaling in resolution and frame access without altering pixel design, as demonstrated in\nexisting high-resolution flat-panel displays and large-area imagers.5 Gate-driver ICs and shift-register\nlogic can be directly interfaced for real-time pixel selection, preserving signal timing and refresh\nstability at scale.\n4. Frame Rate\nFrame rate performance is determined by two factors: the electrical readout bandwidth and the intrinsic\nphotoresponse decay of the Evo-SynT. Crucially, the device's synaptic plasticity timescale is tunable\nby modulating optical or electrical input parameters, allowing the platform to operate in either fast-\nsensing or memory-retention modes. This adaptability supports low-latency real-time imaging as well\nas slow-decay integration for applications such as event-based or ambient-scene memory.6\nCollectively, these strategies establish a scalable path forward for the EROS system. The core pixel\nstructure, circuit topology, and device physics support high-density integration with minimal\narchitectural modification, offering a practical foundation for extending the platform to megapixel-scale\nneuromorphic vision arrays.\nSupplementary references\ncolour-converted micro-LED display with a 122% NTSC gamut. Light-Adv. Manuf. 3, 36\n(2022).\nImage Sensors. Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging Sensors and Systems.\n93-1 (2021).\nSingle-Ended Cyclic ADCs. IEEE Trans. Electron Devices 11, 2414-2422 (2009).\nmatrix flat-panel imagers using compensation lines. Med. Phys. 8, 1855-1864 (2000).\nMaterials for Application: From Lab to Industry. Electronics 13, 241 (2024).\n15, 3257 (2024).\nComment 5: While my paper highlights the advantages of the IGZO-based platform, a direct\ncomparison of actual NIR sensitivity, response speed, SNR, power consumption, and other numerical\nfeatures with existing 2D materials, perovskite, and organic phototransistor-based NIR devices was not\nconducted. Performance benchmarking (responsivity, detectivity, retention time) is necessary to truly\nevaluate the competitiveness of the proposed structure.\nReply\n To directly address the reviewer’s suggestion, we have constructed a new comparison table\nbenchmarking the key performance metrics of our device against recently reported NIR-responsive\nsynaptic transistors based on various material platforms, including metal-oxide, 2D materials, organic\nsemiconductors, and perovskites, as shown in Table R2. While the reviewer pointed out multiple\nmetrics such as NIR sensitivity, response speed, SNR, power consumption, responsivity, detectivity,\nand retention time, we focused our benchmarking on widely reported and comparable figures of merit—\nnamely responsivity and energy consumption. In addition, we included pixel array size and recognition\naccuracy to provide a more comprehensive evaluation of system-level performance.\nFor accurate cross-device comparison, we recalculated key figures using unified definitions and\nstandardized equations, as follows:\n𝑃𝑅 =\n𝐽𝑃ℎ𝑜𝑡𝑜\n(1)\n𝑃𝑃ℎ𝑜𝑡𝑜\n𝐸 = 𝑉×𝐼×𝑡 (2)\nHere, 𝐽 refers to the photocurrent density, 𝑃 denotes the incident light power density, 𝑉 is the\n𝑃ℎ𝑜𝑡𝑜 𝑃ℎ𝑜𝑡𝑜\nprogramming voltage amplitude, 𝐼 is the device current, and 𝑡 is the pulse duration. These quantities\nwere extracted from our experimental results shown in Fig. 2f, g and revised Supplementary Fig. 9.\nThe IGZO/UCNP/IGZO Evo-SynT device achieves responsivities of 116.37 A/W (808 nm) and\n57.48 A/W (940 nm), with corresponding energy consumptions of 77.7 pJ and 25.03 pJ. These features,\ncoupled with the inherent spectral selectivity and noise filtering enabled by the upconversion\nmechanism, underscore the architectural and functional competitiveness of our approach.\nThe benchmarking table has been newly included in the revised supplementary section, along with\nexplanatory notes detailing the equations used and the specific figures from which the input values were\nderived. This update directly supports a fair and quantitative assessment of the proposed platform’s\nperformance relative to existing NIR synaptic devices.\nTable R2. Comparison of key performance metrics of NIR-sensitive synaptic devices\nKey performance\nEnergy\nClass Materials Wavelength Responsivity Pixel Accuracy Ref.\nconsumption\n[nm] [A/W] array [%]\n[J]\nMetal-\nIGZO/Ag2O 830 0.65 - - - 1\noxide\nIGZO/Au NPs 780 3.62 - - 93.8 2\nTa2NiSe5/SnS2 980 14.4 7.88 p - - 3\n2D\nWSe2/InAs 1060 1.134 33.5 p - 96.55 4\nSEBS/\nOrganic 808 1.73 - 10×10 - 5\nPDBT-co-TT\nPEDOT:PSS/\nPerovskite 785 - 3.84 p - 90.53 6\nFaSnI3\n116.37 77.7 p 84.81\nMetal- (808 nm) (808 nm) (808 nm) This\nIGZO/UCNP/IGZO 808, 940 12×12\noxide 57.48 25.03 90.71 work\n(940 nm) (940 nm) (940 nm)\nReferences\nTransistor with Large Conduction States Over 512. Adv. Funct. Mater. 34, 2402222 (2024).\nplasmonic nanoparticles. Nat. Commun. 13, 5081 (2021).\nTa2NiSe5/SnS2 heterojunction from ultraviolet to near-infrared. Light-Sci. Appl. 14, 122\n(2025).\nContaining Multi‐Dimensional van der Waals Heterojunctions. Small 21, 2410892 (2025).\nPhototransistor Arrays. ACS Appl. Mater. Interfaces 16, 36678-36687 (2024).\nHeterojunctions for Neuromorphic Photosensors. Adv. Funct. Mater. 34, 2315175 (2024).\nRevisions in the manuscript\n(1) Page 11\n[Newly added text]\nA performance comparison with recently reported NIR synaptic devices (Supplementary Table 1)\nconfirms that Evo-SynT outperforms existing technologies in terms of responsivity, energy efficiency,\nand recognition accuracy.\n(2) Supplementary Information\n[Newly added table]\nSupplementary Table 1. Comparison of key performance metrics of NIR-sensitive synaptic devices.\nKey performance\nEnergy\nClass Materials Wavelength Responsivity Pixel Accuracy Ref.\nconsumption\n[nm] array\n[A/W] [%]\n[J]\nMetal-\nIGZO/Ag2O 830 0.65 - - - 1\noxide\nIGZO/Au NPs 780 3.62 - - 93.8 2\nTa2NiSe5/SnS2 980 14.4 7.88 p - - 3\n2D\nWSe2/InAs 1060 1.134 33.5 p - 96.55 4\nSEBS/\nOrganic 808 1.73 - 10×10 - 5\nPDBT-co-TT\nPEDOT:PSS/\nPerovskite 785 - 3.84 p - 90.53 6\nFaSnI3\n116.37 77.7 p 84.81\nMetal- (808 nm) (808 nm) (808 nm) This\nIGZO/UCNP/IGZO 808, 940 12×12\noxide 57.48 25.03 90.71 work\n(940 nm) (940 nm) (940 nm)\n[Newly added text]\nResponsivity values were calculated using the equation (1),\n𝑃𝑅 =\n𝐽𝑃ℎ𝑜𝑡𝑜\n(1)\n𝑃𝑃ℎ𝑜𝑡𝑜\nwhere 𝐽 is the photocurrent density and 𝑃 is the incident NIR power density. Photocurrent values\nphoto photo\nwere extracted from experimental transfer characteristics under 808 nm and 940 nm illumination\n(Fig. 2f, g).\nEnergy consumption was estimated using the equation (2),\n𝐸 = 𝑉×𝐼×𝑡 (2)\nwhere 𝑉 is the applied bias (1 V), 𝐼 is the peak postsynaptic current during stimulation, and 𝑡 is the\nstimulation duration (0.5 s). Peak current values were obtained from transient synaptic response plots\n(Supplementary Fig. 9).\nSupplementary references\nTransistor with Large Conduction States Over 512. Adv. Funct. Mater. 34, 2402222 (2024).\nplasmonic nanoparticles. Nat. Commun. 13, 5081 (2021).\nTa2NiSe5/SnS2 heterojunction from ultraviolet to near-infrared. Light-Sci. Appl. 14, 122 (2025).\nContaining Multi‐Dimensional van der Waals Heterojunctions. Small 21, 2410892 (2025).\nPhototransistor Arrays. ACS Appl. Mater. Interfaces 16, 36678-36687 (2024).\nHeterojunctions for Neuromorphic Photosensors. Adv. Funct. Mater. 34, 2315175 (2024).\nComment 6: For image detection simulation work, it is unclear whether the data is obtained from actual\nhardware or is a simple function-fitting-based simulation, and its performance advantage over software\nhas not been clearly proven.\nReply\n We would like to clarify that our simulations are strictly hardware-calibrated, utilizing experimental\ndata extracted directly from the fabricated Evo-SynT devices in EROS array rather than simple arbitrary\nfunction fitting. Furthermore, the proposed platform offers distinct architectural advantages over pure\nsoftware approaches in terms of spectral capability and processing efficiency.\n- Verification of simulation based on actual hardware data\n1) For in-sensor preprocessing (Fig. 5): The simulation incorporates the experimentally measured\noptoelectronic transfer characteristics of the Evo-SynT device in EROS array (Fig. 2f-g). The response\nfunction I(x) (Eq. 5 in the manuscript) was derived by fitting actual drain current measured under\nvarying NIR power densities, ensuring the simulation reflects the device's physical sensitivity.\n2) For in-memory post-processing (Fig. 6): The hardware-related variations used in the CrossSim-based\ninference simulation were not generated from abstract mathematical assumptions. Instead, the\nconductance values were obtained from experimentally measured 10 devices responses, including the\nfull ΔPSC→ G characteristics under different programming states. These measured conductance\nmatrices were directly imported into CrossSim as the noise source for each α-level simulation, ensuring\nthat the perturbations applied to the ViT-Tiny model faithfully reflect realistic device non-idealities.\nTherefore, the model degradation, confusion matrices, and t-SNE distributions reported in this study\nrepresent hardware-constrained inference behavior rather than synthetic or purely formula-derived\nsimulations.\n- Performance advantage over software\nThe EROS platform demonstrates fundamental advantages that software alone cannot achieve:\n1) Spectral information acquisition (sensing advantage): Standard software-based image enhancement\nis limited by the information capture of the sensor. If a conventional visible-light sensor cannot detect\nNIR signals in low-light environments, software cannot recover this missing information. The EROS\narray physically converts invisible NIR photons (808 nm and 940 nm) into electrical signals at the\nsensor level, providing rich environmental data that pure software approaches would completely miss.\n2) In-sensor/in-memory computing efficiency (processing advantage): While software relies on the von\nNeumann architecture (shuttling data between memory and processor), the EROS array performs analog\nin-memory computing. In the preprocessing stage, enhancement occurs instantaneously through the\nphysical photo-response of the array. In the classification stage, the array performs Matrix-Vector\nMultiplication (MVM) in parallel via Ohm’s and Kirchhoff’s laws. This architecture significantly\nreduces data movement latency and power consumption compared to executing the same algorithms on\ndigital software processors.\nThese results collectively demonstrate that our simulation pipeline is tightly constrained by real\nhardware behavior at both sensing and computing levels, and that the proposed platform offers unique\nadvantages that are fundamentally unattainable with software-only approaches. We have now revised\nthe manuscript text accordingly to clear the data is obtained from actual hardware.\nRevisions in the manuscript\n(1) Page 14\n[Newly added text]\nHere, all 𝑥 values directly extracted from the experimental NIR power conditions applied to the physical\nEvo-SynT devices in EROS array. This approach ensures that the simulated image response is\nquantitatively grounded in real hardware behavior.\n(2) Page 15\n[Newly added text]\nThe extracted α-values served as direct inputs for the CrossSim inference simulations presented, thereby\nensuring that the modeled synaptic variations reproduce experimentally observed programming errors.\nResponse to the reviewers’ comments\nNature Communications\nPrevious Manuscript ID: NCOMMS-25-72858A\nRetinocortical in-sensor vision neuromorphic platform for NIR-\naugmented artificial vision\nCorresponding author:\nHyun Jae Kim\nFull mailing address:\nSchool of Electrical and Electronic Engineering,\nYonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea\nTelephone:\n82-2-2123-5865\nE-mail address:\nhjk3@yonsei.ac.kr\nReviewer #2 Comments:\nMy comments have been satisfactorily addressed. The revised manuscript has been greatly enhanced.\nTherefore, I recommend it for acceptance after minor revisions.\n1) For Ref. [54], the first author should be corrected to “Hu, L.”, and the publication year should be\n2021 rather than 2020.\nResponse to the reviewer #2 comments\nComment 1: For Ref. [54], the first author should be corrected to “Hu, L.”, and the publication year\nshould be 2021 rather than 2020.\nReply\n We have changed the first author and publication year of Ref. [54].\nRevisions in the manuscript\n(1) Page 22\n[Before revision]\nComputing. Adv. Funct. Mater. 31, 2005582 (2020).\n[After revision]\nAdv. Funct. Mater. 31, 2005582 (2021)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The paper can be accepted now.", "reviewer_2": "My comments have been satisfactorily addressed. The revised manuscript has been greatly enhanced. Therefore, I recommend it for acceptance after minor revisions.\n1) For Ref. [54], the first author should be corrected to “Hu, L.”, and the publication year should be 2021 rather than 2020.", "reviewer_3": "The reviewer addressed my concerns and made improvements. I recommend publishing the current version."}, "author_response": "Response to the reviewers’ comments\nNature Communications\nPrevious Manuscript ID: NCOMMS-25-72858A\nRetinocortical in-sensor vision neuromorphic platform for NIR-\naugmented artificial vision\nCorresponding author:\nHyun Jae Kim\nFull mailing address:\nSchool of Electrical and Electronic Engineering,\nYonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea\nTelephone:\n82-2-2123-5865\nE-mail address:\nhjk3@yonsei.ac.kr\nReviewer #2 Comments:\nMy comments have been satisfactorily addressed. The revised manuscript has been greatly enhanced.\nTherefore, I recommend it for acceptance after minor revisions.\n1) For Ref. [54], the first author should be corrected to “Hu, L.”, and the publication year should be\n2021 rather than 2020.\nResponse to the reviewer #2 comments\nComment 1: For Ref. [54], the first author should be corrected to “Hu, L.”, and the publication year\nshould be 2021 rather than 2020.\nReply\n We have changed the first author and publication year of Ref. [54].\nRevisions in the manuscript\n(1) Page 22\n[Before revision]\nComputing. Adv. Funct. Mater. 31, 2005582 (2020).\n[After revision]\nAdv. Funct. Mater. 31, 2005582 (2021)."}]

Decision letter