An Oxide Schottky Junction Artificial Optoelectronic Synapse

Perovskite-Doped Modulated Color-Selective Photosynaptic Transistors for Target Object Recognition

The processing of multicolor noisy images in visual neuromorphic devices requires selective absorption at specific wavelengths; however, it is difficult to achieve this because the spectral absorption range of the device is affected by the type of material. Surprisingly, the absorption range of perovskite materials can be adjusted by doping. Herein, a CdCl2 co-doped CsPbBr3 nanocrystal-based photosensitive synaptic transistor (PST) is reported. By decreasing the doping concentration, the response of the PST to short-wavelength light is gradually enhanced, and even weak light of 40 μW·cm-2 can be detected. Benefiting from the excellent color selectivity of the PST device, the device array is applied to feature extraction of target blue items and removal of red and green noise, which results in the recognition accuracy of 95% for the noisy MNIST data set. This work provides new ideas for the application of novel transistors integrating sensors and storage computing.

Unveiling cutting-edge developments: architectures and nanostructured materials for application in optoelectronic artificial synapses

One possible result of low-level characteristics in the traditional von Neumann formulation system is brain-inspired photonics technology based on human brain idea. Optoelectronic neural devices, which are accustomed to imitating the sensory role of biological synapses by adjusting connection measures, can be used to fabricate highly reliable neurologically calculating devices. In this case, nanosized materials and device designs are attracting attention since they provide numerous potential benefits in terms of limited cool contact, rapid transfer fluidity, and the capture of photocarriers. In addition, the combination of classic nanosized photodetectors with recently generated digital synapses offers promising results in a variety of practical applications, such as data processing and computation. Herein, we present the progress in constructing improved optoelectronic synaptic devices that rely on nanomaterials, for example, 0-dimensional (quantum dots), 1-dimensional, and 2-dimensional composites, besides the continuously developing mixed heterostructures. Furthermore, the challenges and potential prospects linked with this field of study are discussed in this paper.

A memristive-photoconductive transduction methodology for accurately nondestructive memory readout

Crossbar resistive memory architectures enable high-capacity storage and neuromorphic computing, accurate retrieval of the stored information is a prerequisite during read operation. However, conventional electrical readout normally suffer from complicated process, inaccurate and destructive reading due to crosstalk effect from sneak path current. Here we report a memristive-photoconductive transduction (MPT) methodology for precise and nondestructive readout in a memristive crossbar array. The individual devices present dynamic filament form/fuse for resistance modulation under electric stimulation, which leads to photogenerated carrier transport for tunable photoconductive response under subsequently light pulse stimuli. This coherent signal transduction can be used to directly detect the memorized on/off states stored in each cell, and a prototype 4 * 4 crossbar memories has been constructed and validated for the fidelity of crosstalk-free readout in recall process.

Bioinspired Artificial Visual-Respiratory Synapse as Multimodal Scene Recognition System with Oxidized-Vacancies MXene

In the pursuit of artificial neural systems, the integration of multimodal plasticity, memory retention, and perceptual functions stands as a paramount objective in achieving neuromorphic perceptual components inspired by the human brain, to emulating the neurological excitability tuning observed in human visual and respiratory collaborations. Here, an artificial visual-respiratory synapse is presented with monolayer oxidized MXene (VRSOM) exhibiting synergistic light and atmospheric plasticity. The VRSOM enables to realize facile modulation of synaptic behaviors, encompassing postsynaptic current, sustained photoconductivity, stable facilitation/depression properties, and "learning-experience" behavior. These performances rely on the privileged photocarrier trapping characteristics and the hydroxyl-preferential selectivity inherent of oxidized vacancies. Moreover, environment recognitions and multimodal neural network image identifications are achieved through multisensory integration, underscoring the potential of the VRSOM in reproducing human-like perceptual attributes. The VRSOM platform holds significant promise for hardware output of human-like mixed-modal interactions and paves the way for perceiving multisensory neural behaviors in artificial interactive devices.

Effect of annealing temperature on the optoelectrical synapse behaviors of A-ZnO microtube

Optoelectronic synapses with fast response, low power consumption, and memory function hold great potential in the future of artificial intelligence technologies. Herein, a strategy of annealing in oxygen ambient at different temperatures is presented to improve the optoelectronic synaptic behaviors of acceptor-rich ZnO (A-ZnO) microtubes. The basic synaptic functions of as-grown and annealed A-ZnO microtubes including excitatory postsynaptic current (EPSC), short-term memory (STM) to long-term memory (LTM) conversion, and paired-pulse facilitation (PPF), were successfully emulated. The results show that the annealing temperature of 600 °C yields high figures of merit compared to other annealed A-ZnO microtubes. The 4-fold and 20-fold enhancement dependent on the light pulse duration time and energy density have been achieved in the 600 °C annealed A-ZnO microtube, respectively. Furthermore, the device exhibited a PPF index of up to 238% and achieved four cycles of "learning-forgetting" process, proving its capability for optical information storage. The free exciton (FX) and donor-acceptor pair (DAP) concentrations significantly influenced the persistent photoconductivity (PPC) behavior of A-ZnO microtubes. Therefore, the LTM response can be controlled by the adjustment of numbers, powers, and interval time of the optical stimulation. This work outlines a strategy to improve the EPSC response through defect control, representing a step towards applications in the field of optoelectronic synaptic device.

Si/CuO Heterojunction-Based Photomemristor for Reconfigurable, Non-Volatile, and Self-Powered In-Sensor Computing

In-sensor computing has attracted considerable interest as a solution for overcoming the energy efficiency and response time limitations of the traditional von Neumann architecture. Recently, emerging memristors based on transition-metal oxides (TMOs) have attracted attention as promising candidates for in-memory computing owing to their tunable conductance, high speed, and low operational energy. However, the poor photoresponse of TMOs presents challenges for integrating sensing and processing units into a single device. This integration is crucial for eliminating the need for a sensor/processor interface and achieving energy-efficient in-sensor computing systems. In this study, a Si/CuO heterojunction-based photomemristor is proposed that combines the reversible resistive switching behavior of CuO with the appropriate optical absorption bandgap of the Si substrate. The proposed photomemristor demonstrates a simultaneous reconfigurable, non-volatile, and self-powered photoresponse, producing a microampere-level photocurrent at zero bias. The controlled migration of oxygen vacancies in CuO result in distinct energy-band bending at the interface, enabling multiple levels of photoresponsivity. Additionally, the device exhibits high stability and ultrafast response speed to the built-in electric field. Furthermore, the prototype photomemristor can be trained to emulate the attention-driven nature of the human visual system, indicating the tremendous potential of TMO-based photomemristors as hardware foundations for in-sensor computing.

A Dual-Functional Integration of Photodetectors and Artificial Optoelectronic Synapses on a VO2/WO3 Heterojunction Device

Bionic visual systems require multimodal integration of eye-like photodetectors and brain-like image memory. However, the integration of photodetectors (PDs) and artificial optoelectronic synapses devices (OESDs) by one device remains a giant challenge due to their photoresponse discrepancy. Herein, a dual-functional integration of PDs and OESDs based on VO2/WO3 heterojunctions is presented. The device can be able to realize a dual-mode conversion between PDs and OESDs through tuning the bias voltage. Under zero bias voltage, the device exhibiting excellent photodetecting behaviors based on the photovoltaic effect, showing a high self-powered photoresponsivity of 18.5 mA W-1 and high detectivity of 7.5 × 1010 Jones with fast photoresponse. When the external bias voltages are applied, it can be acted as an OESD and exhibit versatile electrical and photonic synaptic characteristics based on the trapping and detrapping effects, including synaptic plasticity and learning-experience behaviors. More importantly, benefiting from the excellent photosensing ability and transporting properties, the device shows ultralow-power consumption of 39.0 pJ and a 4 × 4 OESDs array is developed to realize the visual perception and memory. This work not only supplies a novel route to realize complex functional integration just in one device, but also offers effective strategies for developing neuromorphic visual system.

Human somatosensory systems based on sensor-memory-integrated technology

As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing

Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of traditional von Neumann computing architecture. In this work, an adjustable multistage resistance switching ferroelectric Bi2FeCrO6 diode artificial synaptic device was fabricated using a sol-gel method with a simple process. The device exhibits nonlinearity in its electrical characteristics, demonstrating tunable multistage resistance switching behavior and a strong ferroelectric diode effect through the manipulation of ferroelectric polarization. One of its salient advantages resides in its capacity to dynamically regulate its polarization state in response to an external electric field, thereby facilitating the fine-tuning of synaptic connection strength while maintaining synaptic stability. The device is capable of accurately simulating the fundamental properties of biological synapses, including long/short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity. Additionally, the device exhibits a distinctive photoelectric response and is capable of inducing synaptic plasticity by light signal activation. The utilization of a femtosecond laser for the scrutiny of carrier transport mechanisms imparts profound insights into the intricate dynamics governing the optical memory effect. Furthermore, utilizing a convolutional neural network (CNN) architecture, the recognition accuracy of the MNIST and fashion MNIST datasets was improved to 95.6% and 78%, respectively, through the implementation of improved random adaptive algorithms. These findings present a new opportunity for utilizing Bi2FeCrO6 materials in the development of artificial synapses for neuromorphic computation.

Optoelectronic Synapse Based on 2D Electron Gas in Stoichiometry-Controlled Oxide Heterostructures

Emulating synaptic functionalities in optoelectronic devices is significant in developing artificial visual-perception systems and neuromorphic photonic computing. Persistent photoconductivity (PPC) in metal oxides provides a facile way to realize the optoelectronic synaptic devices, but the PPC performance is often limited due to the oxygen vacancy defects that release excess conduction electrons without external stimuli. Herein, a high-performance optoelectronic synapse based on the stoichiometry-controlled LaAlO3/SrTiO3 (LAO/STO) heterostructure is developed. By increasing La/Al ratio up to 1.057:1, the PPC is effectively enhanced but suppressed the background conductivity at the LAO/STO interface, achieving strong synaptic behaviors. The spectral noise analyses reveal that the synaptic behaviors are attributed to the cation-related point defects and their charge compensation mechanism near the LAO/STO interface. The short-term and long-term plasticity is demonstrated, including the paired-pulse facilitation, in the La-rich LAO/STO device upon exposure to UV light pulses. As proof of concepts, two essential synaptic functionalities, the pulse-number-dependent plasticity and the self-noise cancellation, are emulated using the 5 × 5 array of La-rich LAO/STO synapses. Beyond the typical oxygen deficiency control, the results show how harnessing the cation stoichiometry can be used to design oxide heterostructures for advanced optoelectronic synapses and neuromorphic applications.

Light-Induced Conductance Potentiation and Depression in an All-Optically Controlled Memristor

Optoelectronic memristors are new multifunctional devices with both electrically tunable and light-tunable synaptic plasticity, attracting great attention as key promising devices for optoelectronic neuromorphic computing systems. At present, the conductance modulation in most optoelectronic memristors is conducted in a hybrid photoelectric mode, suffering some problems such as heat generation and control complexity. Here, an optoelectronic memristor based on the p+-Si/n-ZnO heterojunction is proposed where the conductance can be reversibly modulated in an all-optically controlled mode. The electron detrapping/trapping mechanism at the p+-Si/n-ZnO interface barrier region is presented to explain the light-induced conductance potentiation/depression behavior. Furthermore, some synaptic functions, including excitatory postsynaptic current (EPSC), inhibitory postsynaptic current (IPSC), and paired-pulse facilitation (PPF), are successfully mimicked in the p+-Si/n-ZnO heterojunction memristor, instructing its application potential for optoelectronic neuromorphic computing.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Intrinsic Defect-Driven Synergistic Synaptic Heterostructures for Gate-Free Neuromorphic Phototransistors

The optoelectronic synaptic devices based on two-dimensional (2D) materials offer great advances for future neuromorphic visual systems with dramatically improved integration density and power efficiency. The effective charge capture and retention are considered as one vital prerequisite to realizing the synaptic memory function. However, the current 2D synaptic devices are predominantly relied on materials with artificially-engineered defects or intricate gate-controlled architectures to realize the charge trapping process. These approaches, unfortunately, suffer from the degradation of pristine materials, rapid device failure, and unnecessary complication of device structures. To address these challenges, an innovative gate-free heterostructure paradigm is introduced herein. The heterostructure presents a distinctive dome-like morphology wherein a defect-rich Fe7S8 core is enveloped snugly by a curved MoS2 dome shell (Fe7S8@MoS2), allowing the realization of effective photocarrier trapping through the intrinsic defects in the adjacent Fe7S8 core. The resultant neuromorphic devices exhibit remarkable light-tunable synaptic behaviors with memory time up to ≈800 s under single optical pulse, thus demonstrating great advances in simulating visual recognition system with significantly improved image recognition efficiency. The emergence of such heterostructures foreshadows a promising trajectory for underpinning future synaptic devices, catalyzing the realization of high-efficiency and intricate visual processing applications.

Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception

The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.

Monolayer Vacancy-Induced MXene Memory for Write-Verify-Free Programming

The fundamental logic states of 1 and 0 in Complementary Metal-Oxide-Semiconductor (CMOS) are essential for modern high-speed non-volatile solid-state memories. However, the accumulated storage signal in conventional physical components often leads to data distortion after multiple write operations. This necessitates a write-verify operation to ensure proper values within the 0/1 threshold ranges. In this work, a non-gradual switching memory with two distinct stable resistance levels is introduced, enabled by the asymmetric vertical structure of monolayer vacancy-induced oxidized Ti3C2Tx MXene for efficient carrier trapping and releasing. This non-cumulative resistance effect allows non-volatile memories to attain valid 0/1 logic levels through direct reprogramming, eliminating the need for a write-verify operation. The device exhibits superior performance characteristics, including short write/erase times (100 ns), a large switching ratio (≈3 × 104), long cyclic endurance (>104 cycles), extended retention (>4 × 106 s), and highly resistive stability (>104 continuous write operations). These findings present promising avenues for next-generation resistive memories, offering faster programming speed, exceptional write performance, and streamlined algorithms.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.

Polarization-Dependent Memory and Erasure in Quantum Dots/Graphene Synaptic Devices

We demonstrate excitatory and inhibitory properties in a single heterostructure consisting of two quantum dots/graphene synaptic elements using linearly polarized monochromatic light. Perovskite quantum dots and PbS quantum dots were used to increase and decrease photocurrent weights, respectively. The polarization-dependent photocurrent was realized by adding a polarizer in the middle of the PbS quantum dots/graphene and perovskite quantum dots/graphene elements. When linearly polarized light passed through the polarizer, both the lower excitatory and upper inhibitory devices were activated, with the lower device with the stronger response dominating to increase the current weight. In contrast, the polarized light was blocked by the polarizer, and the above device was only operated, reducing the current weight. Furthermore, two orthogonal polarizations of light were used to perform the sequential processes of potentiation and habituation. By adjustment of the polarization angle of light, not only the direction of the current weight but also its level was altered.

Light and voltage dual-modulated volatile resistive switching in single ZnO nanowires

A single ZnO nanowire device with volatile resistive switching behavior has been prepared. Different from traditional resistive switching devices, such ZnO nanowire devices do not exhibit resistive switching behaviors under a single bias voltage, and appear resistive switching behavior under the combined action of light stimuli and bias voltage. Through the demonstration of the time-dependent hysteresis curve and atmosphere-dependent hysteresis loop of the resistive switching devices, it is believed that under the resistive switching process, ultraviolet illumination can increase the carrier concentration and modulate the barrier depletion structure, and external bias voltage can ionize the surface state. They work together to modulate the switching process of the devices. Such light stimuli and bias voltage dual-modulated resistive switching device enables optical control and may thus be considered for sensory applications or optically tunable memories.

Ion-Mediated Recombination Dynamics in Perovskite-Based Memory Light-Emitting Diodes for Neuromorphic Control Systems

Neuromorphic devices can help perform memory-heavy tasks more efficiently due to the co-localization of memory and computing. In biological systems, fast dynamics are necessary for rapid communication, while slow dynamics aid in the amplification of signals over noise and regulatory processes such as adaptation- such dual dynamics are key for neuromorphic control systems. Halide perovskites exhibit much more complex phenomena than conventional semiconductors due to their coupled ionic, electronic, and optical properties which result in modulatable drift, diffusion of ions, carriers, and radiative recombination dynamics. This is exploited to engineer a dual-emitter tandem device with the requisite dual slow-fast dynamics. Here, a perovskite-organic tandem light-emitting diode (LED) capable of modulating its emission spectrum and intensity owing to the ion-mediated recombination zone modulation between the green-emitting quasi-2D perovskite layer and the red-emitting organic layer is introduced. Frequency-dependent response and high dynamic range memory of emission intensity and spectra in a LED are demonstrated. Utilizing the emissive read-out, image contrast enhancement as a neuromorphic pre-processing step to improve pattern recognition capabilities is illustrated. As proof of concept using the device's slow-fast dynamics, an inhibition of the return mechanism is physically emulated.

Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor

The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104  s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.

Photonic Physical Reservoir Computing with Tunable Relaxation Time Constant

Recent years have witnessed a rising demand for edge computing, and there is a need for methods to decrease the computational cost while maintaining a high learning performance when processing information at arbitrary edges. Reservoir computing using physical dynamics has attracted significant attention. However, currently, the timescale of the input signals that can be processed by physical reservoirs is limited by the transient characteristics inherent to the selected physical system. This study used an Sn-doped In2 O3 /Nb-doped SrTiO3 junction to fabricate a memristor that could respond to both electrical and optical stimuli. The results show that the timescale of the transient current response of the device could be controlled over several orders of magnitude simply by applying a small voltage. The computational performance of the device as a physical reservoir is evaluated in an image classification task, demonstrating that the learning accuracy could be optimized by tuning the device to exhibit appropriate transient characteristics according to the timescale of the input signals. These results are expected to provide deeper insights into the photoconductive properties of strontium titanate, as well as support the physical implementation of computing systems.

Optically driven intelligent computing with ZnO memristor

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.

Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing

Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.

Multifunctional Multigate One-Transistor with Thin Advanced Materials, Logic-in-Memory, and Artificial Synaptic Behaviors

The high device density and fabrication complexity have hampered the development of the electronics. The advanced designs, which could implement the functions of the circuits with higher device density but less fabrication complexity, are hence required. Meanwhile, the MoS2-based devices have recently attracted considerable attention owing to their advantages such as the ultrathin thickness. However, the MoS2-based multifunctional multigate one-transistor (MGT) designs with logic-in-memory and artificial synaptic functions have rarely been reported. Here, an MGT structure based on the MoS2 channel is proposed, with both the logic-in-memory and artificial synaptic behaviors and with more controllable processes than the manual transfer. The proposed MoS2-based MGT functions could be attributed to the semijunction mechanism and enhanced effect of the additional terminals with improved controllability. This study is the first to demonstrate that the neuromorphic computing, logic gate, and memory functions can all be achieved in a MoS2 MGT device without using any additional layers or plasticity to a transistor. The reported results provide a new strategy for developing brain-like systems and next-generation electronics using multifunctional designs and ultrathin materials.

Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction

Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS2/MoS2 hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS2/MoS2 phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.

Trainable Bilingual Synaptic Functions in Bio-enabled Synaptic Transistors

The signal transmission of the nervous system is regulated by neurotransmitters. Depending on the type of neurotransmitter released by presynaptic neurons, neuron cells can either be excited or inhibited. Maintaining a balance between excitatory and inhibitory synaptic responses is crucial for the nervous system's versatility, elasticity, and ability to perform parallel computing. On the way to mimic the brain's versatility and plasticity traits, creating a preprogrammed balance between excitatory and inhibitory responses is required. Despite substantial efforts to investigate the balancing of the nervous system, a complex circuit configuration has been suggested to simulate the interaction between excitatory and inhibitory synapses. As a meaningful approach, an optoelectronic synapse for balancing the excitatory and inhibitory responses assisted by light mediation is proposed here by deploying humidity-sensitive chiral nematic phases of known polysaccharide cellulose nanocrystals. The environment-induced pitch tuning changes the polarization of the helicoidal organization, affording different hysteresis effects with the subsequent excitatory and inhibitory nonvolatile behavior in the bio-electrolyte-gated transistors. By applying voltage pulses combined with stimulation of chiral light, the artificial optoelectronic synapse tunes not only synaptic functions but also learning pathways and color recognition. These multifunctional bio-based synaptic field-effect transistors exhibit potential for enhanced parallel neuromorphic computing and robot vision technology.

A scalable solution recipe for a Ag-based neuromorphic device

Integration and scalability have posed significant problems in the advancement of brain-inspired intelligent systems. Here, we report a self-formed Ag device fabricated through a chemical dewetting process using an Ag organic precursor, which offers easy processing, scalability, and flexibility to address the above issues to a certain extent. The conditions of spin coating, precursor dilution, and use of solvents were varied to obtain different dewetted structures (broadly classified as bimodal and nearly unimodal). A microscopic study is performed to obtain insight into the dewetting mechanism. The electrical behavior of selected bimodal and nearly unimodal devices is related to the statistical analysis of their microscopic structures. A capacitance model is proposed to relate the threshold voltage (Vth) obtained electrically to the various microscopic parameters. Synaptic functionalities such as short-term potentiation (STP) and long-term potentiation (LTP) were emulated in a representative nearly unimodal and bimodal device, with the bimodal device showing a better performance. One of the cognitive behaviors, associative learning, was emulated in a bimodal device. Scalability is demonstrated by fabricating more than 1000 devices, with 96% exhibiting switching behavior. A flexible device is also fabricated, demonstrating synaptic functionalities (STP and LTP).

High-performance asymmetric electrode structured light-stimulated synaptic transistor for artificial neural networks

Photonics neuromorphic computing shows great prospects due to the advantages of low latency, low power consumption and high bandwidth. Transistors with asymmetric electrode structures are receiving increasing attention due to their low power consumption, high optical response, and simple preparation technology. However, intelligent optical synapses constructed by asymmetric electrodes are still lacking systematic research and mechanism analysis. Herein, we present an asymmetric electrode structure of the light-stimulated synaptic transistor (As-LSST) with a bulk heterojunction as the semiconductor layer. The As-LSST exhibits superior electrical properties, photosensitivity and multiple biological synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, and long-term memory. Benefitting from the asymmetric electrode configuration, the devices can operate under a very low drain voltage of 1 × 10-7 V, and achieve an ultra-low energy consumption of 2.14 × 10-18 J per light stimulus event. Subsequently, As-LSST implemented the optical logic function and associative learning. Utilizing As-LSST, an artificial neural network (ANN) with ultra-high recognition rate (over 97.5%) of handwritten numbers was constructed. This work presents an easily-accessible concept for future neuromorphic computing and intelligent electronic devices.

Silicon-Nanomembrane-Based Broadband Synaptic Phototransistors for Neuromorphic Vision

Neuromorphic vision has been attracting much attention due to its advantages over conventional machine vision (e.g., lower data redundancy and lower power consumption). Here we develop synaptic phototransistors based on the silicon nanomembrane (Si NM), which are coupled with lead sulfide quantum dots (PbS QDs) and poly(3-hexylthiophene) (P3HT) to form a heterostructure with distinct photogating. Synaptic phototransistors with optical stimulation have outstanding synaptic functionalities ranging from ultraviolet (UV) to near-infrared (NIR). The broadband synaptic functionalities enable an array of synaptic phototransistors to achieve the perception of brightness and color. In addition, an array of synaptic phototransistors is capable of simultaneous sensing, processing, and memory, which well mimics human vision.

Optical Memory, Switching, and Neuromorphic Functionality in Metal Halide Perovskite Materials and Devices

Metal halide perovskite based materials have emerged over the past few decades as remarkable solution-processable optoelectronic materials with many intriguing properties and potential applications. These emerging materials have recently been considered for their promise in low-energy memory and information processing applications. In particular, their large optical cross-sections, high photoconductance contrast, large carrier-diffusion lengths, and mixed electronic/ionic transport mechanisms are attractive for enabling memory elements and neuromorphic devices that are written and/or read in the optical domain. Here, recent progress toward memory and neuromorphic functionality in metal halide perovskite materials and devices where photons are used as a critical degree of freedom for switching, memory, and neuromorphic functionality is reviewed.

Reversible Photomodulation of Two-Dimensional Electron Gas in LaAlO3/SrTiO3 Heterostructures

Long-lived photoinduced conductance changes in LaAlO₃/SrTiO₃ (LAO/STO) heterostructures enable their use in optoelectronic memory applications. However, it remains challenging to quench the persistent photoconductivity (PPC) instantly and reproducibly, which limits the reversible optoelectronic switching. Herein, we demonstrate a reversible photomodulation of two-dimensional electron gas (2DEG) in LAO/STO heterostructures with high reproducibility. By irradiating UV pulses, the 2DEG at the LAO/STO interface is gradually transformed to the PPC state. Notably, the PPC can be completely removed by water treatment when two key requirements are met: (1) the moderate oxygen deficiency in STO and (2) the minimal band edge fluctuation at the interface. Through our X-ray photoelectron spectroscopy and electrical noise analysis, we reveal that the reproducible change in the conductivity of 2DEG is directly attributed to the surface-driven electron relaxation in the STO. Our results provide a stepping-stone toward developing optically tunable memristive devices based on oxide 2DEG systems.

Artificial Intelligence and Advanced Materials

Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.

Bimodal ionic photomemristor based on a high-temperature oxide superconductor/semiconductor junction

Memristors, a cornerstone for neuromorphic electronics, respond to the history of electrical stimuli by varying their electrical resistance across a continuum of states. Much effort has been recently devoted to developing an analogous response to optical excitation. Here we realize a novel tunnelling photo-memristor whose behaviour is bimodal: its resistance is determined by the dual electrical-optical history. This is obtained in a device of ultimate simplicity: an interface between a high-temperature superconductor and a transparent semiconductor. The exploited mechanism is a reversible nanoscale redox reaction between both materials, whose oxygen content determines the electron tunnelling rate across their interface. The redox reaction is optically driven via an interplay between electrochemistry, photovoltaic effects and photo-assisted ion migration. Besides their fundamental interest, the unveiled electro-optic memory effects have considerable technological potential. Especially in combination with high-temperature superconductivity which, in addition to facilitating low-dissipation connectivity, brings photo-memristive effects to the realm of superconducting electronics.

Organic Optoelectronic Synapses for Sound Perception

The neuromorphic systems for sound perception is under highly demanding for the future bioinspired electronics and humanoid robots. However, the sound perception based on volume, tone and timbre remains unknown. Herein, organic optoelectronic synapses (OOSs) are constructed for unprecedented sound recognition. The volume, tone and timbre of sound can be regulated appropriately by the input signal of voltages, frequencies and light intensities of OOSs, according to the amplitude, frequency, and waveform of the sound. The quantitative relation between recognition factor (ζ) and postsynaptic current (I = Ilight - Idark) is established to achieve sound perception. Interestingly, the bell sound for University of Chinese Academy of Sciences is recognized with an accuracy of 99.8%. The mechanism studies reveal that the impedance of the interfacial layers play a critical role in the synaptic performances. This contribution presents unprecedented artificial synapses for sound perception at hardware levels.

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.

A photonic artificial synapse with a reversible multifaceted photochromic compound

Modern computational technology based on the von Neumann architecture physically partitions memory and the central processing unit, resulting in fundamental speed limitations and high energy consumption. On the other hand, the human brain is an extraordinary multifunctional organ composed of more than a billion neurons capable of simultaneously thinking, processing, and storing information. Neurons are interconnected with synapses that control information flow from pre-synaptic-to-post-synaptic neurons. Therefore, emulating synaptic functionalities and developing neuromorphic computational architecture has recently attracted much interest. Due to their high-speed, large bandwidth, and no interconnect-related power loss, photonic (all-optical) synapses can overcome the existing hurdles with electronic synapses. Here, we show an artificial photonic synapse by utilizing the well-established reversible, high-contrast photochromic organic compound, spiropyran, stimulated by optical pulses. Optical transmission of spiropyran significantly changes during spiropyran-merocyanine isomerization driven by UV-visible optical pulses. Such changes are equivalent to the biological synapses' inhibitory and excitatory synaptic actions. The slow relaxation to the initial state is considered as synaptic plasticity responsible for learning and memory formation. Short-term memory (STM), long-term memory (LTM), and transition from the STM to the LTM are demonstrated in all-optical synapses by modulating the stimuli's strength. The solvatochromic properties of spiropyran are further utilized to augment memory in synapses. Our work shows that photochromic organic compounds are excellent hosts for artificial photonic synapses and can be implemented in neuromorphic applications.

Optoelectronic Memristive Synapse Behavior for the Architecture of Cu2ZnSnS4@BiOBr Embedded in Poly(methyl methacrylate)

The great potential of artificial optoelectronic devices that are capable of mimicking biosynapse functions in brain-like neuromorphic computing applications has aroused extensive interest, and the architecture design is decisive yet challenging. Herein, a new architecture of p-type Cu₂ZnSnS₄@BiOBr nanosheets embedded in poly(methyl methacrylate) (PMMA) films (CZTS@BOB-PMMA) is presented acting as a switching layer, which not only shows the bipolar resistive switching features (SET/RESET voltages, ∼ -0.93/+1.35 V; retention, >10⁴ s) and electrical- and near-infrared light-induced synapse plasticity but also demonstrates electrical-driven excitatory postsynaptic current, spiking-time-dependent plasticity, paired pulse facilitation, long-term plasticity, long- and short-term memory, and "learning-forgetting-learning" behaviors. The approach is a rewarding attempt to broaden the research of optoelectric controllable memristive devices for building neuromorphic architectures mimicking human brain functionalities.

Wavelength and Polarization Sensitive Synaptic Phototransistor Based on Organic n-type Semiconductor/Supramolecular J-Aggregate Heterostructure

Human retina- and brain-inspired optoelectronic synapses, which integrate light detection and signal memory functions for data processing, have significant interest because of their potential applications for artificial vision technology. In nature, many animals such as mantis shrimp use polarized light information as well as scalar information including wavelength and intensity; however, a spectropolarimetric organic optoelectronic synapse has been seldom investigated. Herein, we report an organic synaptic phototransistor, consisting of a charge trapping liquid-crystalline perylene bisimide J-aggregate and a charge transporting crystalline dichlorinated naphthalene diimide, that can detect both wavelength and polarization information. The device shows persistent positive and negative photocurrents under low and high voltage conditions, respectively. Furthermore, the aligned organic heterostructure in the thin-film enables linearly polarized light to be absorbed with a dichroic ratio of 1.4 and 3.7 under transverse polarized blue and red light illumination, respectively. These features allow polarized light sensitive postsynaptic functions in the device. Consequently, a simple polarization imaging sensor array is successfully demonstrated using photonic synapses, which suggests that a supramolecular material is an important candidate for the development of spectropolarimetric neuromorphic vision systems.

CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing

Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO₂/TiO₂/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 10⁴ s, and endurance of 10⁶ cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application.

In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array

Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaO_x (a-GaO_x) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaO_x photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.

Progress of Materials and Devices for Neuromorphic Vision Sensors

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.

In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing

As machine vision technology generates large amounts of data from sensors, it requires efficient computational systems for visual cognitive processing. Recently, in-sensor computing systems have emerged as a potential solution for reducing unnecessary data transfer and realizing fast and energy-efficient visual cognitive processing. However, they still lack the capability to process stored images directly within the sensor. Here, we demonstrate a heterogeneously integrated 1-photodiode and 1 memristor (1P-1R) crossbar for in-sensor visual cognitive processing, emulating a mammalian image encoding process to extract features from the input images. Unlike other neuromorphic vision processes, the trained weight values are applied as an input voltage to the image-saved crossbar array instead of storing the weight value in the memristors, realizing the in-sensor computing paradigm. We believe the heterogeneously integrated in-sensor computing platform provides an advanced architecture for real-time and data-intensive machine-vision applications via bio-stimulus domain reduction.

Designing in-sensor computing systems remains a challenge. Here, the authors demonstrate artificial optical neurons based on the in-sensor computing architecture that fuses sensory and computing nodes into a single platform capable of reducing data transfer time and energy for encoding and classification.

A photosensor employing data-driven binning for ultrafast image recognition

Pixel binning is a technique, widely used in optical image acquisition and spectroscopy, in which adjacent detector elements of an image sensor are combined into larger pixels. This reduces the amount of data to be processed as well as the impact of noise, but comes at the cost of a loss of information. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single “superpixel” that extends over the whole face of the chip. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm. We demonstrate the classification of optically projected images from the MNIST dataset on a nanosecond timescale, with enhanced dynamic range and without loss of classification accuracy. Our concept is not limited to imaging alone but can also be applied in optical spectroscopy or other sensing applications.

Field-Created Coordinate Cation Bridges Enable Conductance Modulation and Artificial Synapse within Metal Nanoparticles

When metal nanoparticles are functionalized with charged ligands, the movement of counterions and conduction electrons is coupled, which enables us to develop electronic devices, including diodes, transistors, and logic gates, but dynamically modulating the conductivity of a synaptic device within these materials has proved challenging. Here we show that an artificial synapse can be created from thin films of functionalized metal nanoparticles using an active silver electrode. The electric-field-injected Ag⁺ coordinates with carboxyl ligands that sets up a conduction bridge to increase the nanoparticle conductivity by reducing the electron tunneling/hopping energy barriers. The dynamic modulation of conductivity allows us to implement several important synaptic functions such as potentiation/depression, paired-pulse facilitation, learning behaviors including short-term to long-term memory transition, self-learning, and massed leaning vs spaced learning. Finally, based on the nonvolatile characteristics, the metal nanoparticle synapse is used to build a single-layer hardware spiking neural network (SNN) for pattern recognition.

Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing

In order to imitate brain-inspired biological information processing systems, various neuromorphic computing devices have been proposed, most of which were prepared on rigid substrates and have energy consumption levels several orders of magnitude higher than those of biological synapses (∼10 fJ per spike). Herein, a new type of wearable organic ferroelectric artificial synapse is proposed, which has two modulation modes (optical and electrical modulation). Because of the high photosensitivity of organic semiconductors and the ultrafast polarization switching of ferroelectric materials, the synaptic device has an ultrafast operation speed of 30 ns and an ultralow power consumption of 0.0675 aJ per synaptic event. Under combined photoelectric modulation, the artificial synapse realizes associative learning. The proposed artificial synapse with ultralow power consumption demonstrates good synaptic plasticity under different bending strains. This provides new avenues for the construction of ultralow power artificial intelligence system and the development of future wearable devices.

Recent Advances in Transistor-Based Bionic Perceptual Devices for Artificial Sensory Systems

The sensory nervous system serves as the window for human beings to perceive the outside world by converting external stimuli into distinctive spiking trains. The sensory neurons in this system can process multimodal sensory signals with extremely low power consumption. Therefore, new-concept devices inspired by the sensory neuron are promising candidates to address energy issues in nowadays’ robotics, prosthetics and even computing systems. Recent years have witnessed rapid development in transistor-based bionic perceptual devices, and it is urgent to summarize the research and development of these devices. In this review, the latest progress of transistor-based bionic perceptual devices for artificial sense is reviewed and summarized in five aspects, i.e., vision, touch, hearing, smell, and pain. Finally, the opportunities and challenges related to these areas are also discussed. It would have bright prospects in the fields of artificial intelligence, prosthetics, brain-computer interface, robotics, and medical testing.