Physical reservoir computing (RC) utilizes intrinsic dynamics of physical systems for efficient data processing. Emerging optoelectronic RC platforms merge the benefits of electronic and photonic computation. However, conventional designs are constrained by the unipolar photoresponse of optoelectronic devices, limiting reservoir state diversity and computational accuracy. Here we present an all-optically controlled RC system employing highly uniform and stable oxide memristor arrays. The devices exhibit wavelength-dependent bipolar photoresponse, originating from light-induced dynamic evolution of oxygen vacancies. Tuning the power density and irradiation mode of dual-wavelength light enables dynamic control of photocurrent relaxation and nonlinearity. We thus develop bipolar and parallel coding strategies to significantly enhance reservoir dynamics and nonlinear mapping capability. In word recognition and time-series prediction tasks, the bipolar coding demonstrates markedly improved accuracy compared to unipolar coding. The parallel coding supports multi-source signal fusion within a single reservoir, maintaining high accuracy while significantly reducing hardware consumption. This work provides a high-performance physical RC scheme, paving the way for intelligent edge computing.

Reservoir computing (RC) has emerged as an efficient neuromorphic framework for temporal information processing, offering low training complexity and hardware-friendly implementation. Memristors' nonlinear dynamics and input-dependent memory effects make them ideal candidates for high-performance physical RC. Based on their conductance modulation, memristors can be classified as electronic or optoelectronic types. However, no systematic review has compared electrically and optically controlled memristive RC. This review fills that gap by comparing them from the device to the system level. We first summarize the resistive switching mechanisms of electronic and optoelectronic memristors, highlighting their distinct roles in RC encoding and processing temporal signals. We then review recent advances in electronic memristive RC, emphasizing architecture innovations and performance improvements in pattern recognition and sequence prediction. Subsequently, we focus on optoelectronic memristive RC, where the high parallelism of optical inputs are harnessed for color vision processing, dynamic gesture recognition, and multi-signals fusion. Notably, we provide a systematic comparison between single-modal and multi-modal RC implementations, demonstrating how hybrid electro-optical stimulation enhances feature diversity and task accuracy. Finally, we outline key challenges and future research directions, including the development of fully hardware-integrated RC systems, system-level multi-modal RC architectures, and novel encoding paradigms.

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energy-efficient computing is increasingly growing.The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution.Neuromorphic computing mimics the working princi-ples of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hard-ware foundation for the development of new generation AI technology.Artificial neurons and synapses are the two core compo-nents of neuromorphic computing systems.Artificial perception is a crucial aspect of neuromorphic computing,where artifi-cial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research.This work reviews recent advances in artificial sensory neurons and their applications.First,biological sensory neurons are briefly described.Then,differ-ent types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working mechanisms.Next,the research progress of artificial sensory neurons and their applications in arti-ficial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and smell.Finally,challenges faced by artificial sensory neurons at both device and system levels are summarized.

Abstract Brain‐inspired neuromorphic computing is recognized as a promising technology for implementing human intelligence in hardware. Neuromorphic devices, including artificial synapses and neurons, are regarded as essential components for the construction of neuromorphic hardware systems. Recently, optoelectronic neuromorphic devices are increasingly highlighted due to their potential applications in next‐generation artificial visual systems, attributed to their integrated sensing, computing, and memory capabilities. In this review, recent advancements in optoelectronic synapses and neurons are examined, with an emphasis on their structural characteristics, operational principles, and the replication of neuromorphic functions. For optoelectronic synaptic devices, such as memristor‐ and transistor‐based ones, attention is given to the two primary weight update modes: the light‐electricity synergistic mode and the all‐optical mode. Optoelectronic neurons are discussed in terms of different device types, including threshold switch neurons and semiconductor laser neurons. Last, the challenges that impede the progress of optoelectronic neuromorphic devices are identified, and potential future directions are suggested.

Vision sensors are becoming increasingly ubiquitous, and they continuously collect, store, communicate, and process vast amount of sensitive data that are vulnerable to being stolen and misused. Existing cryptosystems based on complex cipher algorithms generally require extensive computational resources, making them difficult to use in vision sensors that have limited processing capabilities. Here, we propose and experimentally demonstrate a novel in situ image cryptography scheme based on a neuromorphic vision sensor comprising all-optically controlled (AOC) memristors. Due to the unique light wavelength and irradiation history-dependent bidirectional persistent photoconductivity of AOC memristors, a visual image can be stored, encrypted, decrypted, denoised, and destroyed within a vision sensor. A decrypted image can be encoded in situ and then accurately recognized through a memristive neural network. Encrypted and destroyed images are capable of withstanding hacking attacks even with trained neural networks. Our cryptography scheme enables complete cryptographic operations entirely on a sensor and, therefore, effectively safeguards visual information. This work provides a simple yet efficient solution to the security challenges faced by vision sensors.

Threshold switching (TS) memristors are promising candidates for artificial neurons in neuromorphic systems. However, they often lack biological plausibility, typically functioning solely in an excitation mode. The absence of an inhibitory mode limits neurons' ability to synergistically process both excitatory and inhibitory synaptic signals. To address this limitation, we propose a novel memristive neuron capable of operating in both excitation and inhibition modes. The memristor's threshold voltage can be reversibly tuned using voltages of different polarities because of its bipolar TS behavior, enabling the device to function as an electronically reconfigurable bi-mode neuron. A variety of neuronal activities such as all-or-nothing behavior and tunable firing probability are mimicked under both excitatory and inhibitory stimuli. Furthermore, we develop a self-adaptive neuromorphic vision sensor based on bi-mode neurons, demonstrating effective object recognition in varied lighting conditions. Thus, our bi-mode neuron offers a versatile platform for constructing neuromorphic systems with rich functionality.

Optoelectronic synapses can perceive both optical and electrical signals, which are critical for the realization of neuromorphic computing. We have rationally designed an optoelectronic synaptic transistor based on amorphous ZnAlSnO for multi-target neuromorphic simulation and recognition. The dual-input models are well operated by applying light pulses on the channel and electric pulses on the gate, and the transformation from short-term potentiation (STP) to long-turn potentiation (LTP) is identified for tunable synaptic plasticity. In the electrical operation mode, a single-layer artificial neural network was established to recognize handwritten digits by LTP/LTD (long-turn depression) modulation, with a recognition accuracy of 89.2% for the actual device. In the optical operation mode, the processes of repetitive learning, image recognition, and biased/correlated random-walk learning are simulated on the basis of frequency, quantity, and power of light, with an energy consumption per event as low as 4.3 pJ. This work will facilitate the development of future artificial synapses and highlights the potential of amorphous oxide semiconductors for next-generation computer hardware applications.

Retina-inspired visual sensors play a crucial role in the realization of neuromorphic visual systems. Nevertheless, significant obstacles persist in the pursuit of achieving bidirectional synaptic behavior and attaining high performance in the context of photostimulation. In this study, we propose a reconfigurable all-optical controlled synaptic device based on the IGZO/SnO/SnS heterostructure, which integrates sensing, storage and processing functions. Relying on the simple heterojunction stack structure and the role of energy band engineering, synaptic excitatory and inhibitory behaviors can be observed under the light stimulation of ultraviolet (266 nm) and visible light (405, 520 and 658 nm) without additional voltage modulation. In particular, junction field-effect transistors based on the IGZO/SnO/SnS heterostructure were fabricated to elucidate the underlying bidirectional photoresponse mechanism. In addition to optical signal processing, an artificial neural network simulator based on the optoelectrical synapse was trained and recognized handwritten numerals with a recognition rate of 91%. Furthermore, we prepared an 8 × 8 optoelectrical synaptic array and successfully demonstrated the process of perception and memory for image recognition in the human brain, as well as simulated the situation of damage to the retina by ultraviolet light. This work provides an effective strategy for the development of high-performance all-optical controlled optoelectronic synapses and a practical approach to the design of multifunctional artificial neural vision systems.

Conductive-bridge random access memory (CBRAM) emerges as a promising candidate for next-generation memory and storage device. However, CBRAMs are prone to degenerate and fail during electrochemical metallization processes. To address this issue, herein we propose a self-repairability strategy for CBRAMs. Amorphous NbSe2 was designed as the resistive switching layer, with Cu and Au as the top and bottom electrodes, respectively. The NbSe2 CBRAMs demonstrate exceptional cycle-to-cycle and device-to-device uniformity, with forming-free and compliance current-free resistive switching characteristics, low-operation voltage, and competitive endurance and retention performance. Most importantly, the self-repairable behavior is discovered for the first time in CBRAM. The device after failure can recover its performance to the initially normal state by operating with a slightly large reset voltage. The existence of Cu conductive filament and excellent controllability of Cu migration in the NbSe2 switching layer has been revealed by a designed broken-down point approach, which is responsible for the self-repairable behavior of NbSe2 CBRAMs. Our self-repairable and high-uniform amorphous NbSe2 CBRAM may open the door to the development of memory and storage devices in the future.

2D-layered materials are recognized as up-and-coming candidates to overcome the intrinsic physical limitation of silicon-based devices. Herein, the coexistence of positive persistent photoconductivity (PPPC) and negative persistent photoconductivity (NPPC) in SnSe thin films prepared by pulsed laser deposition provides an excellent avenue for engineering novel devices. It is determined that surface oxygen is co-regulated by physisorption and chemisorption, and the NPPC is attributed to the photo-controllable oxygen desorption behavior. The dominant behavior of chemisorption induces high stability, while physisorption provides room for adjusting NPPC. A simple fully light-modulated artificial synaptic device based on SnSe film is constructed to operate various synaptic plasticity and reversible modulation of conductance by applying 430 and 255 nm illuminations. A three-layer artificial neural network structure with a high accuracy of 95.33% to recognize handwritten digital images is implemented based on the device. Furthermore, the pressure-related cognition response of humans while climbing and the foraging and recognition behaviors of anemonefish are mimicked. This work demonstrates the potential of 2D-layered materials for developing neuromorphic computing and simulating biological behaviors without additional treatment. Furthermore, the one-step method for preparation is highly adaptable and expected to realize large-area growth and integration of SnSe-based devices.

Nowadays,artificial intelligence(AI)is playing an increasingly important role in human society.Running AI algorithms represented by deep learning places great demands on computational power of hardware.However,with Moore's law approaching physical limitations,the traditional von Neumann computing architecture cannot meet the urgent demand for promoting hardware computational power.The brain-inspired neuromorphic computing(NC)employing an integrated processing-memory architecture is expected to provide an important hardware basis for developing novel AI technologies with low energy consumption and high computational power.Under this conception,artificial neurons and synapses,as the core components of NC systems,have become a research hotspot.This paper aims to provide a comprehensive review on the development of oxide neuron devices.Firstly,several mathematical models of neurons are described.Then,recent progress of Hodgkin-Huxley neurons,leaky integrate-and-fire neurons and oscillatory neurons based on oxide electronic devices is introduced in detail.The effects of device structures and working mechanisms on neuronal performance are systematically analyzed.Next,the hardware implementation of spiking neural networks and oscillatory neural networks based on oxide artificial neurons is demonstrated.Finally,the challenges of oxide neuron devices,arrays and networks,as well as prospect for their applications are pointed out.

短程突触可塑性保证了对神经形态计算系统中神经信息处理和短程记忆的动态调节.短程易化特性(STF)在多种人工突触器件中被广泛报道,与之相比短程抑制特性(STD)的研究则进展缓慢,仅有为数不多的器件表现出STD特性,并且其单个刺激下的功耗比生物突触(1～10 fJ)高出许多.本研究提出了一种室温下通过溶液法制备的新型石墨烯/氧化石墨烯/石墨烯(G/GO/G)质子突触器件,该器件在几十毫伏的连续脉冲电压刺激下表现出电导逐渐下降的特性,这种特性源于质子在GO/G界面处连续积累导致的逐渐增强的反向质子电流.G/GO/G器件可以很好地模拟STD突触特性.由于操作电压和电流极小,器件单个刺激下的最低功耗可低至几十阿焦,此超低功耗特性有助于解决散热问题从而实现高度集成的神经形态电路.因此,全碳结构器件在脑内植入神经假体领域有很好的应用前景.

Artificial vision is crucial for most artificial intelligence applications. Conventional artificial visual systems have been facing challenges in terms of real-time information processing due to the physical separation of sensors, memories, and processors, which results in the production of a large amount of redundant data as well as the data conversion and transfer between these three components consuming most of the time and energy. Emergent optoelectronic memristors with the ability to realize integrated sensing-computing-memory (ISCM) are key candidates for solving such challenges and therefore attract increasing attention. At present, the memristive ISCM devices can only perform primary-level computing with external light signals due to the fact that only monotonic increase of memconductance upon light irradiation is achieved in most of these devices. Here, we propose an all-optically controlled memristive ISCM device based on a simple structure of Au/ZnO/Pt with the ZnO thin film sputtered at pure Ar atmosphere. This device can perform advanced computing tasks such as nonvolatile neuromorphic computing and complete Boolean logic functions only by light irradiation, owing to its ability to reversibly tune the memconductance with light. Moreover, the device shows excellent operation stability ascribed to a purely electronic memconductance tuning mechanism. Hence, this study is an important step towards the next generation of artificial visual systems.

Conventional computers based on the von Neumann architecture are inefficient in parallel computing and self-adaptive learning, and therefore cannot meet the rapid development of information technology that needs efficient and high-speed computing. Owing to the unique advantages such as high parallelism and ultralow power consumption, bioinspired neuromorphic computing can have the capability of breaking through the bottlenecks of conventional computers and is now considered as an ideal option to realize the next-generation artificial intelligence. As the hardware carriers that allow the implementing of neuromorphic computing, neuromorphic devices are very critical in building neuromorphic chips. Meanwhile, the development of human visual systems and optogenetics also provides a new insight into how to study neuromorphic devices. The emerging optoelectronic neuromorphic devices feature the unique advantages of photonics and electronics, showing great potential in the neuromorphic computing field and attracting more and more attention of the scientists. In view of these, the main purpose of this review is to disclose the recent research advances in optoelectronic neuromorphic devices and the prospects of their practical applications. We first review the artificial optoelectronic synapses and neurons, including device structural features, working mechanisms, and neuromorphic simulation functions. Then, we introduce the applications of optoelectronic neuromorphic devices particularly suitable for the fields including artificial vision systems, artificial perception systems, and neuromorphic computing. Finally, we summarize the challenges to the optoelectronic neuromorphic devices, which we are facing now, and present some perspectives about their development directions in the future.

In current optical imaging devices, complex peripheral circuits are required to obtain clear and true images, which leads to limited power and area efficiency. Inspired by the visual processing in retina, an optoelectronic neuromorphic device with short‐term plasticity (STP) can realize self‐adjustment of the visual system to real time in response to the external stimuli, including light controlled short‐term facilitation (STF) and short‐term depression (STD). Herein, an optoelectronic synaptic device based on a simple Au/ZnS/Pt structure is found to show both synaptic STD and STF under light stimulation. STD originates from photoexcited carriers trapped by defects in the ZnS film, whereas STF results from photocurrent rising due to trap filling via previous electrical stimulation. Moreover, the STP of the device exhibits excellent stability and repeatability. In addition, this optoelectronic device can be used to adjust the sharpness of the view according to different brightness levels through STD and STF. The realization of self‐adjustment through STP of the emerging optoelectronic neuromorphic device may pave the way for bionic systems facing complex environments.

In article number 2005582, Fei Zhuge and co-workers develop an all-optically controlled (AOC) analog memristor based on the relatively mature material InGaZnO. The memconductance is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. The light-induced multiple memconductance states are nonvolatile. This device has promising applications in AOC spiking neural networks for highly efficient optoelectronic neuromorphic computing.

Neuromorphic computing (NC) is a new generation of artificial intelligence. Memristors are promising candidates for NC owing to the feasibility of their ultrahigh-density 3D integration and their ultralow energy consumption. Compared to traditional electrical memristors, the emerging optoelectronic memristors are more attractive owing to their ability to combine the advantages of both photonics and electronics. However, the inability to reversibly tune the memconductance with light has severely restricted the development of optoelectronic NC. Here, an all-optically controlled (AOC) analog memristor is realized, with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. The device is based on the relatively mature semiconductor material InGaZnO and a memconductance tuning mechanism of light-induced electron trapping and detrapping. It is found that the light-induced multiple memconductance states are nonvolatile. Furthermore, spike-timing-dependent plasticity learning can be mimicked in this AOC memristor, indicating its potential applications in AOC spiking neural networks for highly efficient optoelectronic NC.

Artificial intelligence is widely used in everyday life. However, an insufficient computing efficiency due to the so-called von Neumann bottleneck cannot satisfy the demand for real-time processing of rapidly growing data. Memristive in-memory computing is a promising candidate for highly efficient data processing. However, performance of memristors varies significantly because of microstructure change induced by electric-driven matter migration. Here, we propose an all-optically controlled (AOC) memristor with a simple Au/ZnO/Pt sandwich structure based on a purely electronic tuning mechanism of memconductance. The memconductance can be reversibly tuned only by light irradiation with different wavelengths. The device can be used to perform in-memory computation such as nonvolatile neuromorphic computing and Boolean logic functions. Moreover, no microstructure change is involved during the operation of our AOC memristor which demonstrates superior operation stability. Based on this and its structural simplicity, the device has attractive application prospects for the next generation of computing systems.

Memristors have emerged as key candidates for beyond-von-Neumann neuromorphic or in-memory computing owing to the feasibility of their ultrahigh-density three-dimensional integration and their ultralow energy consumption. A memristor is generally a two-terminal electronic element with conductance that varies nonlinearly with external electric stimuli and can be remembered when the electric power is turned off. As an alternative, light can be used to tune the memconductance and endow a memristor with a combination of the advantages of both photonics and electronics. Both increases and decreases in optically induced memconductance have been realized in different memristors; however, the reversible tuning of memconductance with light in the same device remains a considerable challenge that severely restricts the development of optoelectronic memristors. Here we describe an all-optically controlled (AOC) analog memristor with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. Our memristor is based on the relatively mature semiconductor material InGaZnO (IGZO) and a memconductance tuning mechanism of light-induced electron trapping and detrapping. We demonstrate that spike-timing-dependent plasticity (STDP) learning can be realized in our device, indicating its potential applications in AOC spiking neural networks (SNNs) for highly efficient optoelectronic neuromorphic computing.

采用溶液法制备出石墨烯/氧化石墨烯/石墨烯(G/GO/G)全碳忆阻器,并且探究了Ar气氛环境下退火温度对石墨烯电极的影响.研究结果表明,退火处理可以在一定程度上改善石墨烯电极的电学性能.利用优化后的石墨烯电极构筑的G/GO/G全碳忆阻器具有一次写多次读的忆阻特性.