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Highlights Photonic synapse capable of multispectral color discrimination is demonstrated. Strong excited-state dipoles enable remarkable discrimination intensity (0.05–40 mW cm -2 ). This approach is not restricted to a specific medium in the channel layer, and convolutional neural network with synapses array achieves over 94% inference accuracy for Canadian-Institute-For-Advanced-Research-10 images. To emulate the functionality of the human retina and achieve a neuromorphic visual system, the development of a photonic synapse capable of multispectral color discrimination is of paramount importance. However, attaining robust color discrimination across a wide intensity range, even irrespective of medium limitations in the channel layer, poses a significant challenge. Here, we propose an approach that can bestow the color-discriminating synaptic functionality upon a three-terminal transistor flash memory even with enhanced discriminating capabilities. By incorporating the strong induced dipole moment effect at the excitation, modulated by the wavelength of the incident light, into the floating gate, we achieve outstanding RGB color-discriminating synaptic functionality within a remarkable intensity range spanning from 0.05 to 40 mW cm −2 . This approach is not restricted to a specific medium in the channel layer, thereby enhancing its applicability. The effectiveness of this color-discriminating synaptic functionality is demonstrated through visual pre-processing of a photonic synapse array, involving the differentiation of RGB channels and the enhancement of image contrast with noise reduction. Consequently, a convolutional neural network can achieve an impressive inference accuracy of over 94% for Canadian-Institute-For-Advanced-Research-10 colorful image recognition task after the pre-processing. Our proposed approach offers a promising solution for achieving robust and versatile RGB color discrimination in photonic synapses, enabling significant advancements in artificial visual systems.

Visual adaptation allows organisms to effectively analyze visual information in varying light conditions by autonomously adjusting photosensitivity, which is essential for the visual system to perform accurate perception in complex environments. In order to realistically implement the functionality of the visual system, the exploration of bioinspired electronics with adaptive capability is highly desired. Herein, a self‐powered optoelectronic memristor based on ZnO/WOx heterojunction is developed, which can exhibit the visual adaptation functions of desensitization and Weber's law. These functions are achieved through the coupling of the photovoltaic effect with electron trapping in the space charge region of the heterojunction. Furthermore, a bioinspired visual adaptive neuron has been constructed, comprising an optoelectronic memristor and a NbOx‐based threshold switching memory, capable of directly converting constant light stimuli into dynamic spike trains. Finally, the adaptive image preprocessing is realized, which promotes the improvement of the object recognition accuracy during the overexposed image recognition process. This study offers a novel approach to developing biologically plausible visual adaptation, fostering the future progress of dynamic neuromorphic visual systems. The self‐powered optoelectronic memristor based on ZnO/WOx heterojunction is developed, exhibiting the visual adaptation functions of desensitization and Weber's law. A bioinspired visual adaptive neuron is constructed, capable of converting light stimuli into dynamic spike trains. The adaptive image preprocessing is realized, which promotes the improvement of the object recognition accuracy during the overexposed image recognition process.

Artificial visual neural systems have emerged as promising candidates for overcoming the von Neumann bottleneck via integrating image perception, storage, and computation. Existing photoelectric memristors are limited by the need for specific wavelengths or long input times to maintain stable behavior. Here, we introduce a benzothiophene‐modified covalent organic framework, enhancing the photoelectric response of methyl trinuclear copper for low‐voltage (0.2 V) redox processes. The material enables the modulation of 50 conductive states via light and electrical signals, improving recognition accuracy in low light, dense fog, and high‐frequency motion. The ITO/BTT‐Cu3/ITO device's accuracy increases from 7.1% with 2 states to 87.1% after training. This construction strategy and the synergistic effect of photoelectric interactions offer a new pathway for the development of photoelectric neuromorphic computing elements capable of processing environmental information in situ. Donor‐redox type covalent organic framework (COF) films exhibit photoelectric regulation capability under illumination due to the synergistic effect of electron‐donating groups and light‐induced charge transfer. This characteristic offers the advantages of high efficiency and low power consumption in integrated sensing‐computation‐storage systems for multi‐scenario recognition.

The exponential growth of data has exposed the inherent bottlenecks of the von Neumann architecture—specifically its limited computational efficiency and high energy consumption—necessitating an urgent shift toward innovative hardware solutions. Biological perception systems, particularly the human visual system, serve as a premier model for highly integrated, energy‐efficient, and multimodal processing, providing a critical blueprint for the future of intelligent computing. Field‐effect transistors (FETs) have emerged as a leading platform for visual neuromorphic systems, leveraging their exceptional optoelectronic tunability, mechanical flexibility, and low‐power operation. This review provides a comprehensive overview of FET‐based visual neuromorphic systems, covering semiconductor material selection, fundamental device architectures, and governing operational principles. Then, the critical role of these devices in emulating biological visual functions is detailed. Finally, the prevailing technical challenges and future development prospects for FET‐mediated perception are discussed. This work aims to provide essential insights into the design of the next generation of artificial visual neuromorphic systems and bio‐inspired electronics. This review presents a comprehensive overview of FET‐based visual neuromorphic systems, covering their semiconductor materials, core device architectures, and operating mechanisms. It further reviews their implementation in emulating biological visual functions, addresses current technological challenges, and outlines future development directions. The work aims to inform the design of next‐generation bio‐inspired artificial vision hardware.

Artificial neuromorphic vision systems emulate the biological visual pathway by integrating sensing, storage, and information processing within a unified architecture. Featuring high speed, low power consumption, and superior temporal resolution, they demonstrate significant potential in fields such as autonomous driving, facial recognition, and intelligent perception. As the core building block, the optoelectronic synapse plays a decisive role in determining system performance, which is closely related to its material composition, structural design, and functional characteristics. This review systematically summarizes recent progress in optoelectronic synaptic materials, device architectures, and performance evaluation methodologies. Furthermore, it explores the working mechanisms and network architectures of optoelectronic synapse‐based neuromorphic vision systems, highlighting their capability in image perception, information storage, and target recognition. Current challenges, including environmental stability, large‐scale array fabrication, chip‐level integration, and adaptability of visual functions to real‐world scenarios, are discussed in depth. Finally, the review provides an outlook on future development trends toward stable, scalable, and highly integrated optoelectronic neural vision systems, underscoring their key importance in next‐generation intelligent sensing and information‐processing technologies. This review highlights recent advances in optoelectronic synapses for artificial neuromorphic vision, emphasizing their material systems, structural designs, and performance metrics. It further discusses visual neural networks enabled by these synapses, covering perception, memory, and recognition functionalities, and analyzes challenges in stability, integration, and system adaptability. Finally, it outlines future directions toward large‐scale, intelligent optoelectronic vision systems.

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.

In-sensor and near-sensor computing are becoming the next-generation computing paradigm for high-density and low-power sensory processing. To fulfil a high-density and efficient neuromorphic visual system with fully hierarchical emulation of the retina and visual cortex, emerging multimodal neuromorphic devices for multi-stage processing and a fully hardware-implemented system with versatile image processing functions are still lacking and highly desirable. Here we demonstrate an emerging multimodal-multifunctional resistive random-access memory (RRAM) device array based on modified silk fibroin protein (MSFP), exhibiting both optoelectronic RRAM (ORRAM) mode featured by unique negative and positive photoconductance memory and electrical RRAM (ERRAM) mode featured by analogue resistive switching. A full hardware implementation of the artificial visual system with versatile image processing functions is realised for the first time, including ORRAM mode array for the in-sensor image pre-processing (contrast enhancement, background denoising, feature extraction) and ERRAM mode array for near-sensor high-level image recognition, which hugely improves the integration density, and simply the circuit design and the fabrication and integration complexity. In-sensor and near-sensor computing are emerging as the next-generation computing paradigm, for high-density and low-power sensory processing. Here, the authors report a fully hardware-implemented artificial visual system for versatile image processing based on multimodal-multifunctional optoelectronic resistive memory devices with optical and electrical resistive switching modes.

The human visual system’s adaptability to varying brightness levels has inspired the development of optoelectronic neuromorphic devices. However, achieving bidirectional photoresponse, essential for mimicking these functions, often requires high operation voltages or high light intensities. Here, we propose a bidirectional ZnO/CsPbBr 3 heterostructure based neuromorphic image sensor array (10 × 10 pixels) capable of ultraweak light stimulation. The device demonstrates positive and negative photoconductivity through the ionization and deionization of oxygen vacancies in the ZnO channel, extendable to other ZnO/perovskites and IGZO/perovskites heterostructures. Operating at a reduced bias voltage of 2.0 V, the array achieves synaptic weight updates under green (525 nm) and UV (365 nm) light with light intensities ranging from as low as 45 nW/cm² to 15.69 mW/cm², mimicking basic synaptic functions and visual adaptation. It performs multiple image pre-processing tasks, including background denoising and encoding spatiotemporal motion, achieving 92% accuracy in pattern recognition and 100% accuracy in motion clustering. This straightforward strategy highlights a potential for intelligent visual systems capable of real-time image processing under low voltage and dark conditions. High voltages/light intensities are typically needed to mimic human visual adaptability. Here, the authors present an image sensor array with low operation voltage that mimics synaptic functions with ultraweak light stimulation and performs image processing tasks accurately.

Perceptual enhancement of neural and behavioral response due to combinations of multisensory stimuli are found in many animal species across different sensory modalities. By mimicking the multisensory integration of ocular-vestibular cues for enhanced spatial perception in macaques, a bioinspired motion-cognition nerve based on a flexible multisensory neuromorphic device is demonstrated. A fast, scalable and solution-processed fabrication strategy is developed to prepare a nanoparticle-doped two-dimensional (2D)-nanoflake thin film, exhibiting superior electrostatic gating capability and charge-carrier mobility. The multi-input neuromorphic device fabricated using this thin film shows history-dependent plasticity, stable linear modulation, and spatiotemporal integration capability. These characteristics ensure parallel, efficient processing of bimodal motion signals encoded as spikes and assigned with different perceptual weights. Motion-cognition function is realized by classifying the motion types using mean firing rates of encoded spikes and postsynaptic current of the device. Demonstrations of recognition of human activity types and drone flight modes reveal that the motion-cognition performance match the bio-plausible principles of perceptual enhancement by multisensory integration. Our system can be potentially applied in sensory robotics and smart wearables. Inspired by the multisensory cue integration in macaque’s brain for spatial perception, the authors develop a neuromorphic motion-cognition nerve that achieves cross-modal perceptual enhancement for robotics and wearable applications.

Bioinspired neuromorphic machine vision system (NMVS) that integrates retinomorphic sensing and neuromorphic computing into one monolithic system is regarded as the most promising architecture for visual perception. However, the large intensity range of natural lights and complex illumination conditions in actual scenarios always require the NMVS to dynamically adjust its sensitivity according to the environmental conditions, just like the visual adaptation function of the human retina. Although some opto‐sensors with scotopic or photopic adaption have been developed, NMVSs, especially fully flexible NMVSs, with both scotopic and photopic adaptation functions are rarely reported. Here we propose an ion‐modulation strategy to dynamically adjust the photosensitivity and time‐varying activation/inhibition characteristics depending on the illumination conditions, and develop a flexible ion‐modulated phototransistor array based on MoS2/graphdiyne heterostructure, which can execute both retinomorphic sensing and neuromorphic computing. By controlling the intercalated Li+ ions in graphdiyne, both scotopic and photopic adaptation functions are demonstrated successfully. A fully flexible NMVS consisting of front‐end retinomorphic vision sensors and a back‐end convolutional neural network is constructed based on the as‐fabricated 28 × 28 device array, demonstrating quite high recognition accuracies for both dim and bright images and robust flexibility. This effort for fully flexible and monolithic NMVS paves the way for its applications in wearable scenarios. A flexible phototransistor array that can execute both retinomorphic sensing and neuromorphic computing is developed, demonstrating both scotopic and photopic adaptation functions. Based on this device array, a fully flexible neuromorphic machine vision system consisting of front‐end retinomorphic vision sensors and back‐end convolutional neural networks is constructed, demonstrating high recognition accuracies for dim and bright images.