All-Oxide-Based Highly Transparent Photonic Synapse for Neuromorphic Computing

Ultraviolet Optoelectronic Synapse Based on AlScN/p-i-n GaN Heterojunction for Advanced Artificial Vision Systems

Ferroelectric materials represent a frontier in semiconductor research, offering the potential for novel optoelectronics. AlScN material is a kind of outstanding ferroelectric semiconductor with strong residual polarization, high Curie temperature, and mainstream semiconductor fabrication compatibility. However, it is challenging to realize multi-state optical responders due to their limited light sensitivity. Here, a two-terminal AlScN/p-i-n GaN heterojunction ultraviolet optoelectronic synapse is fabricated, overcoming this limitation by leveraging hole capture at the AlScN/p-GaN hetero-interface for multi-state modulation. The novel structure maintains excellent memristor characteristics based on the ferroelectric of AlScN, realizing an on/off ratio of 9.36 × 105. More importantly, the device can mimic synaptic characteristics essential for artificial vision systems, achieving an image recognition accuracy of 93.7% with a weight evolution nonlinearity of 0.26. This approach not only extends the applications of AlScN in optoelectronics but also paves the way for advanced artificial vision systems with image preprocessing and recognition capabilities. The findings provide a step forward in the development of non-volatile memories with potential for on-chip sensing and computing.

High-Precision Attention Mechanism for Machine Vision Enabled by an Artificial Optoelectronic Memristor Synapse

The rapid advancement of artificial intelligence has facilitated the broad application of machine vision systems in diverse industries. However, these systems are often confronted with computational challenges stemming from an overwhelming amount of data. Here, we have developed a novel optoelectronic memristor synapse constructed from an ITO/Nb:SrTiO3 heterostructure, which synergistically integrates light signal detection with information processing and memory functions. Notably, we have achieved precise decoupling of the interactions between light power and wavelength at the hardware level, significantly enhancing the accuracy and efficiency of image processing. Furthermore, by incorporating an attention mechanism analogous to that of human vision, we have enabled the device to weight key information and filter out irrelevant data. Experimental results demonstrate that this attention mechanism can increase the accuracy of facial recognition by 13% while reducing the data load by 35-65%. This work is expected to advance the development of optoelectronic synapses in machine vision.

Precise weight tuning in quantum dot-based resistive-switching memory for neuromorphic systems

In this study, nonvolatile bipolar resistive switching and synaptic emulation behaviors are performed in an InGaP quantum dots (QDs)/HfO2-based memristor device. First, the physical and chemical properties of InGaP QDs are investigated by high-resolution transmission electron microscopy and spectrophotometric analysis. Through comparative experiments, it is proven that the HfO2 layer improves the variations in resistive switching characteristics. Additionally, the Al/QDs/HfO2/ITO device exhibits reversible switching performances with excellent data retention. Fast switching speeds in the order of nanoseconds were confirmed, which could be explained by trapping/detrapping and quantum tunneling effects by the trap provided by nanoscale InGaP QDs. In addition, the operating voltage is decreased when the device is exposed to ultraviolet light for low-power switching. Biological synapse features such as spike-timing-dependent plasticity are emulated for neuromorphic systems. Finally, the incremental step pulse using proven algorithm method enabled the implementation of four-bit states (16 states), markedly enhancing the inference precision of neuromorphic systems.

Optoelectronic array of photodiodes integrated with RRAMs for energy-efficient in-sensor computing

The rapid development of internet of things (IoT) urgently needs edge miniaturized computing devices with high efficiency and low-power consumption. In-sensor computing has emerged as a promising technology to enable in-situ data processing within the sensor array. Here, we report an optoelectronic array for in-sensor computing by integrating photodiodes (PDs) with resistive random-access memories (RRAMs). The PD-RRAM unit cell exhibits reconfigurable optoelectronic output and photo-responsivity by programming RRAMs into different resistance states. Furthermore, a 3 × 3 PD-RRAM array is fabricated to demonstrate optical image recognition, achieving a universal architecture with ultralow latency and low power consumption. This study highlights the great potential of the PD-RRAM optoelectronic array as an energy-efficient in-sensor computing primitive for future IoT applications.

A Sunlight-Interference-Immune Artificial Optoelectronic Synaptic Device for Visual Perception and Memory

The artificial photoelectric synaptic devices integrating optical signal response and data processing functions enable the simulation of the human visual system. However, the optical modulation behavior of photoelectronic synaptic devices is susceptible to interference from sunlight background, significantly hindering their applications in optical modulation. To address this issue, a Ni/AlYN/ITO structure for sunlight-interference-free artificial synaptic memristor was designed and fabricated, and the application in photoelectric modulation was demonstrated. Solar-blind ultraviolet light with low background noise and high security is introduced as the optical stimuli signal. Various biological synaptic functions, including short-term and long-term synaptic plasticity, have been successfully simulated under the modulation of optical and electrical signals. Furthermore, it emulates the human visual system's perception and memory functions, exhibiting notable memory retention (over 300 s) and capacity (up to 5.6 times after 5 cycles). Notably, the device's output current remains unaffected by the optical signal interference. This artificial photoelectric synaptic device not only extends the potential for human visual perception in the solar-blind ultraviolet region, but also facilitates the integration of solar-blind optical communication and brain-inspired computing. This advancement is expected to enable information reception, transmission, and processing resistant to visible light interference, thereby creating and boosting applications in secure optical communication.

Recent Advancements in 2D Material-Based Memristor Technology Toward Neuromorphic Computing

Two-dimensional (2D) layered materials have recently gained significant attention and have been extensively studied for their potential applications in neuromorphic computing, where they are used to mimic the functions of the human brain. Their unique properties, including atomic-level thickness, exceptional mechanical stability, and tunable optical and electrical characteristics, make them highly versatile for a wide range of applications. In this review, we offer a comprehensive analysis of 2D material-based memristors. Furthermore, we examine the ability of 2D material-based memristors to successfully mimic the human brain by referencing their neuromorphic applications.

Transparent Zinc Oxide Memristor Structures: Magnetron Sputtering of Thin Films, Resistive Switching Investigation, and Crossbar Array Fabrication

This paper presents the results of experimental studies of the influence of high-frequency magnetron sputtering power on the structural and electrophysical properties of nanocrystalline ZnO films. It is shown that at a magnetron sputtering power of 75 W in an argon atmosphere at room temperature, ZnO films have a relatively smooth surface and a uniform nanocrystalline structure. Based on the results obtained, the formation and study of resistive switching of transparent ITO/ZnO/ITO memristor structures as well as a crossbar array based on them were performed. It is demonstrated that memristor structures based on ZnO films obtained at a magnetron sputtering power of 75 W exhibit stable resistive switching for 1000 cycles between high resistance states (HRS = 537.4 ± 26.7 Ω) and low resistance states (LRS = 291.4 ± 38.5 Ω), while the resistance ratio in HRS/LRS is ~1.8. On the basis of the experimental findings, we carried out mathematical modeling of the resistive switching of this structure, and it demonstrated that the regions with an increase in the electric field strength along the edge of the upper electrode become the main sources of oxygen vacancy generation in ZnO film. A crossbar array of 16 transparent ITO/ZnO/ITO memristor structures was also fabricated, demonstrating 20,000 resistive switching cycles between LRS = 13.8 ± 1.4 kΩ and HRS = 34.8 ± 2.6 kΩ for all devices, with a resistance ratio of HRS/LRS of ~2.5. The obtained results can be used in the development of technological processes for the manufacturing of transparent memristor crossbars for neuromorphic structures of machine vision, robotics, and artificial intelligence systems.

Synaptic Feature of Quantum Dot Light-Emitting Diodes for Visualization of Learning Process

Brain-inspired electronics with synaptic functions hold significant promise for advancing artificial intelligent applications. In this study, we demonstrate the synaptic feature of quantum-dot light-emitting diodes (QLEDs), which can convert electrical pulses into synapse-like light signals (the brightness gradually increases as the electrical pulses are prolonged). These features are analogous to learning and forgetting in biological synapses. The enhancement of brightness can be attributed to the reduction of charge transfer from the quantum dots to ZnO electron transport layer and resistive switching effect. With an integrated complementary metal-oxide-semiconductor (CMOS) drive, arrayed synaptic QLEDs can simulate the visualization of brain-like learning processes, which can reduce the noise toward high image recognition rate (>95.0%) by deep neural networks. Our findings introduce a novel brain-inspired optoelectronic approach with potential applications in optical neuromorphic systems.

Optical Bio-Inspired Synaptic Devices

The traditional computer with von Neumann architecture has the characteristics of separate storage and computing units, which leads to sizeable time and energy consumption in the process of data transmission, which is also the famous "von Neumann storage wall" problem. Inspired by neural synapses, neuromorphic computing has emerged as a promising solution to address the von Neumann problem due to its excellent adaptive learning and parallel capabilities. Notably, in 2016, researchers integrated light into neuromorphic computing, which inspired the extensive exploration of optoelectronic and all-optical synaptic devices. These optical synaptic devices offer obvious advantages over traditional all-electric synaptic devices, including a wider bandwidth and lower latency. This review provides an overview of the research background on optoelectronic and all-optical devices, discusses their implementation principles in different scenarios, presents their application scenarios, and concludes with prospects for future developments.

Quantum Dots for Resistive Switching Memory and Artificial Synapse

Memristor devices for resistive-switching memory and artificial synapses have emerged as promising solutions for overcoming the technological challenges associated with the von Neumann bottleneck. Recently, due to their unique optoelectronic properties, solution processability, fast switching speeds, and low operating voltages, quantum dots (QDs) have drawn substantial research attention as candidate materials for memristors and artificial synapses. This review covers recent advancements in QD-based resistive random-access memory (RRAM) for resistive memory devices and artificial synapses. Following a brief introduction to QDs, the fundamental principles of the switching mechanism in RRAM are introduced. Then, the RRAM materials, synthesis techniques, and device performance are summarized for a relative comparison of RRAM materials. Finally, we introduce QD-based RRAM and discuss the challenges associated with its implementation in memristors and artificial synapses.

SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature

1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.

Centimeter-Scale Tellurium Oxide Films for Artificial Optoelectronic Synapses with Broadband Responsiveness and Mechanical Flexibility

Prevailing over the bottleneck of von Neumann computing has been significant attention due to the inevitableness of proceeding through enormous data volumes in current digital technologies. Inspired by the human brain's operational principle, the artificial synapse of neuromorphic computing has been explored as an emerging solution. Especially, the optoelectronic synapse is of growing interest as vision is an essential source of information in which dealing with optical stimuli is vital. Herein, flexible optoelectronic synaptic devices composed of centimeter-scale tellurium dioxide (TeO2) films detecting and exhibiting synaptic characteristics to broadband wavelengths are presented. The TeO2-based flexible devices demonstrate a comprehensive set of emulating basic optoelectronic synaptic characteristics; i.e., excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), conversion of short-term to long-term memory, and learning/forgetting. Furthermore, they feature linear and symmetric conductance synaptic weight updates at various wavelengths, which are applicable to broadband neuromorphic computations. Based on this large set of synaptic attributes, a variety of applications such as logistic functions or deep learning and image recognition as well as learning simulations are demonstrated. This work proposes a significant milestone of wafer-scale metal oxide semiconductor-based artificial synapses solely utilizing their optoelectronic features and mechanical flexibility, which is attractive toward scaled-up neuromorphic architectures.

Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices, opening numerous opportunities across countless domains, including personalized healthcare and advanced robotics. Leveraging 3D integration, edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption. Here, we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications, including electroencephalogram (EEG)-based seizure prediction, electromyography (EMG)-based gesture recognition, and electrocardiogram (ECG)-based arrhythmia detection. With experiments on three biomedical datasets, we observe the classification accuracy improvement for the pretrained model with 2.93% on EEG, 4.90% on ECG, and 7.92% on EMG, respectively. The optical programming property of the device enables an ultra-low power (2.8 × 10-13 J) fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios. Moreover, the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions, making it promising for neuromorphic vision application. To display the benefits of these intricate synaptic properties, a 5 × 5 optoelectronic synapse array is developed, effectively simulating human visual perception and memory functions. The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.

Optically Tunable Electrical Oscillations in Oxide-Based Memristors for Neuromorphic Computing

The application of hardware-based neural networks can be enhanced by integrating sensory neurons and synapses that enable direct input from external stimuli. This work reports direct optical control of an oscillatory neuron based on volatile threshold switching in V3O5. The devices exhibit electroforming-free operation with switching parameters that can be tuned by optical illumination. Using temperature-dependent electrical measurements, conductive atomic force microscopy (C-AFM), in situ thermal imaging, and lumped element modelling, it is shown that the changes in switching parameters, including threshold and hold voltages, arise from overall conductivity increase of the oxide film due to the contribution of both photoconductive and bolometric characteristics of V3O5, which eventually affects the oscillation dynamics. Furthermore, V3O5 is identified as a new bolometric material with a temperature coefficient of resistance (TCR) as high as -4.6% K-1 at 423 K. The utility of these devices is illustrated by demonstrating in-sensor reservoir computing with reduced computational effort and an optical encoding layer for spiking neural network (SNN), respectively, using a simulated array of devices.

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.

Flexible and Energy-Efficient Synaptic Transistor with Quasi-Linear Weight Update Protocol by Inkjet Printing of Orientated Polar-Electret/High-k Oxide Composite Dielectric

Inkjet printing artificial synapse is cost-effective but challenging in emulating synaptic dynamics with a sufficient number of effective weight states under ultralow voltage spiking operation. A synaptic transistor gated by inkjet-printed composite dielectric of polar-electret polyvinylpyrrolidone (PVP) and high-k zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear weight update with a large variation margin is obtained through the coupling effect and the facilitation of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows. Crucial features of biological synapses including long-term plasticity, spike-timing-dependence-plasticity (STDP), "Learning-Experience" behavior, and ultralow energy consumption (<10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1%) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.

Neuromorphic Computing of Optoelectronic Artificial BFCO/AZO Heterostructure Memristors Synapses

Compared with purely electrical neuromorphic devices, those stimulated by optical signals have gained increasing attention due to their realistic sensory simulation. In this work, an optoelectronic neuromorphic device based on a photoelectric memristor with a Bi2FeCrO6/Al-doped ZnO (BFCO/AZO) heterostructure is fabricated that can respond to both electrical and optical signals and successfully simulate a variety of synaptic behaviors, such as STP, LTP, and PPF. In addition, the photomemory mechanism was identified by analyzing the energy band structures of AZO and BFCO. A convolutional neural network (CNN) architecture for pattern classification at the Mixed National Institute of Standards and Technology (MNIST) was used and improved the recognition accuracy of the MNIST and Fashion-MNIST datasets to 95.21% and 74.19%, respectively, by implementing an improved stochastic adaptive algorithm. These results provide a feasible approach for future implementation of optoelectronic synapses.

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.

Site-Specific Emulation of Neuronal Synaptic Behavior in Au Nanoparticle-Decorated Self-Organized TiOx Surface

Neuromorphic computing is a potential approach for imitating massive parallel processing capabilities of a bio-synapse. To date, memristors have emerged as the most appropriate device for designing artificial synapses for this purpose due to their excellent analog switching capacities with high endurance and retention. However, to build an operational neuromorphic platform capable of processing high-density information, memristive synapses with nanoscale footprint are important, albeit with device size scaled down, retaining analog plasticity and low power requirement often become a challenge. This paper demonstrates site-selective self-assembly of Au nanoparticles on a patterned TiOx layer formed as a result of ion-induced self-organization, resulting in site-specific resistive switching and emulation of bio-synaptic behavior (e.g., potentiation, depression, spike rate-dependent and spike timing-dependent plasticity, paired pulse facilitation, and post tetanic potentiation) at nanoscale. The use of local probe-based methods enables nanoscale probing on the anisotropic films. With the help of various microscopic and spectroscopic analytical tools, the observed results are attributed to defect migration and self-assembly of implanted Au atoms on self-organized TiOx surfaces. By leveraging the site-selective evolution of gold-nanostructures, the functionalized TiOx surface holds significant potential in a multitude of fields for developing cutting-edge neuromorphic computing platforms and Au-based biosensors with high-density integration.

Room-Temperature Quantum Diodes with Dynamic Memory for Neural Logic Operations

The pursuit of high-performance, next-generation nanoelectronics is fundamentally reliant on exploiting quantum phenomena such as tunneling at room temperature. However, quantum tunneling and memory dynamics are governed by two conflicting parameters: the presence or absence of defects. Therefore, the integration of both attributes within a single device presents substantial challenges. Nevertheless, successful integration has the potential to prompt crucial breakthroughs by emulating biobrain-like dynamics, in turn enabling sophisticated in-material neural logic operations. In this work, we demonstrate that a conformal nanolaminate HfO2/ZrO2 structure on silicon enables high-performing (>106 s) Fowler-Nordheim tunneling at room temperature. In addition, the device exhibits unipolar dynamic hysteresis loop opening (on/off ratio >102) with high endurance (>104 cycles) along with negative differential resistance, which is attributed to the collective ferroelectric and capacitive effects and is utilized to emulate synaptic functions. Further, proof-of-concept logic gates based on voltage-dependent plasticity and time-domain were developed using a single device, offering in-material neural-like data processing. These findings pave the way for the realization of high-performing and scalability tunneling devices in advanced nanoelectronics, which mark a promising route toward the development of next-generation, fundamental neural logic computing systems.

Light-Stimulated IGZO Transistors with Tunable Synaptic Plasticity Based on Casein Electrolyte Electric Double Layer for Neuromorphic Systems

In this study, optoelectronic synaptic transistors based on indium-gallium-zinc oxide (IGZO) with a casein electrolyte-based electric double layer (EDL) were examined. The casein electrolyte played a crucial role in modulating synaptic plasticity through an internal proton-induced EDL effect. Thus, important synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, and spike rate-dependent and spike number-dependent plasticity, were successfully implemented by utilizing the persistent photoconductivity effect of the IGZO channel stimulated by light. The synergy between the light stimulation and the EDL effect allowed the effective modulation of synaptic plasticity, enabling the control of memory levels, including the conversion of short-term memory to long-term memory. Furthermore, a Modified National Institute of Standards and Technology digit recognition simulation was performed using a three-layer artificial neural network model, achieving a high recognition rate of 90.5%. These results demonstrated a high application potential of the proposed optoelectronic synaptic transistors in neuromorphic visual systems.

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.

Chiroptical Synaptic Heterojunction Phototransistors Based on Self-Assembled Nanohelix of π-Conjugated Molecules for Direct Noise-Reduced Detection of Circularly Polarized Light

High-performance chiroptical synaptic phototransistors are successfully demonstrated using heterojunctions composed of a self-assembled nanohelix of a π-conjugated molecule and a metal oxide semiconductor. To impart strong chiroptical activity to the device, a diketopyrrolopyrrole-based π-conjugated molecule decorated with chiral glutamic acid is newly synthesized; this molecule is capable of supramolecular self-assembly through noncovalent intermolecular interactions. In particular, nanohelix formed by intertwinded fibers with strong and stable chiroptical activity in a solid-film state are obtained through hydrogen-bonding-driven, gelation-assisted self-assembly. Phototransistors based on interfacial charge transfer at the heterojunction from the chiroptical nanohelix to the metal oxide semiconductor show excellent chiroptical detection with a high photocurrent dissymmetry factor of 1.97 and a high photoresponsivity of 218 A W-1 . The chiroptical phototransistor demonstrates photonic synapse-like, time-dependent photocurrent generation, along with persistent photoconductivity, which is attributed to the interfacial charge trapping. Through the advantage of synaptic functionality, a trained convolutional neural network successfully recognizes noise-reduced circularly polarized images of handwritten alphabetic characters with better than 89.7% accuracy.

Photoactivated organic phosphorescence by stereo-hindrance engineering for mimicking synaptic plasticity

Purely organic phosphorescent materials with dynamically tunable optical properties and persistent luminescent characteristics enable more novel applications in intelligent optoelectronics. Herein, we reported a concise and universal strategy to achieve photoactivated ultralong phosphorescence at room temperature through stereo-hindrance engineering. Such dynamically photoactivated phosphorescence behavior was ascribed to the suppression of non-radiative transitions and improvement of spin-orbit coupling (SOC) as the variation of the distorted molecular conformation by the synergistic effect of electrostatic repulsion and steric hindrance. This "trainable" phosphorescent behavior was first proposed to mimic biological synaptic plasticity, especially for unique experience-dependent plasticity, by the manipulation of pulse intensity and numbers. This study not only outlines a principle to design newly dynamic phosphorescent materials, but also broadens their utility in intelligent sensors and robotics.

All oxide based flexible multi-folded invisible synapse as vision photo-receptor

All oxide-based transparent flexible memristor is prioritized for the potential application in artificially simulated biological optoelectronic synaptic devices. SnOx memristor with HfOx layer is found to enable a significant effect on synaptic properties. The memristor exhibits good reliability with long retention, 104 s, and high endurance, 104 cycles. The optimized 6 nm thick HfOx layer in SnOx-based memristor possesses the excellent synaptic properties of stable 350 epochs training, multi-level conductance (MLC) behaviour, and the nonlinearity of 1.53 and 1.46 for long-term potentiation and depression, respectively, and faster image recognition accuracy of 100% after 23 iterations. The maximum weight changes of -73.12 and 79.91% for the potentiation and depression of the synaptic device, respectively, are observed from the spike-timing-dependent plasticity (STDP) characteristics making it suitable for biological applications. The flexibility of the device on the PEN substrate is confirmed by the acceptable change of nonlinearities up to 4 mm bending. Such a synaptic device is expected to be used as a vision photo-receptor.

On-Chip Photonic Synapses with All-Optical Memory and Neural Network Computation

Inspired by the human brain, neural network computing was expected to break the bottleneck of traditional computing, but the integrated design still faces great challenges. Here, a readily integrated membrane-system photonic synapse was demonstrated. By pre-pulse training at 1064 nm (cutoff wavelength), the photonic synapse can be regulated both excitatory and inhibitory at tunable wavelengths (1200-2000 nm). Furthermore, more weights and memory functions were shown through the photonic synapse integrated network. Additionally, the digital recognition function of the single-layer perceptron neural network constructed by photonic synapses has been successfully demonstrated. Most of the biological synaptic functions were realized by the photonic synaptic network, and it had the advantages of compact structure, scalable, adjustable wavelength, and so on, which opens up a new idea for the study of the neural synaptic network.

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.

Near-Infrared Artificial Optical Synapse Based on the P(VDF-TrFE)-Coated InAs Nanowire Field-Effect Transistor

Optical synapse is the basic component for optical neuromorphic computing and is attracting great attention, mainly due to its great potential in many fields, such as image recognition, artificial intelligence and artificial visual perception systems. However, optical synapse with infrared (IR) response has rarely been reported. InAs nanowires (NWs) have a direct narrow bandgap and a large surface to volume ratio, making them a promising material for IR detection. Here, we demonstrate a near-infrared (NIR) (750 to 1550 nm) optical synapse for the first time based on a poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE))-coated InAs NW field-effect transistor (FET). The responsivity of the P(VDF-TrFE)-coated InAs NW FET reaches 839.3 A/W under 750 nm laser illumination, demonstrating the advantage of P(VDF-TrFE) coverage. The P(VDF-TrFE)-coated InAs NW device exhibits optical synaptic behaviors in response to NIR light pulses, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF) and a transformation from short-term plasticity (STP) to long-term plasticity (LTP). The working mechanism is attributed to the polarization effect in the ferroelectric P(VDF-TrFE) layer, which dominates the trapping and de-trapping characteristics of photogenerated holes. These findings have significant implications for the development of artificial neural networks.

Advances in Emerging Photonic Memristive and Memristive-Like Devices

Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive-like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive-like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive-like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive-like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive-like devices, and lay a foundation for academic research and commercial applications.

Optical and optoelectronic neuromorphic devices based on emerging memory technologies

As artificial intelligence continues its rapid development, inevitable challenges arise for the mainstream computing hardware to process voluminous data (Big data). The conventional computer system based on von Neumann architecture with separated processor unit and memory is approaching the limit of computational speed and energy efficiency. Thus, novel computing architectures such as in-memory computing and neuromorphic computing based on emerging memory technologies have been proposed. In recent years, light is incorporated into computational devices, beyond the data transmission in traditional optical communications, due to its innate superiority in speed, bandwidth, energy efficiency, etc. Thereinto, photo-assisted and photoelectrical synapses are developed for neuromorphic computing. Additionally, both the storage and readout processes can be implemented in optical domain in some emerging photonic devices to leverage unique properties of photonics. In this review, we introduce typical photonic neuromorphic devices rooted from emerging memory technologies together with corresponding operational mechanisms. In the end, the advantages and limitations of these devices originated from different modulation means are listed and discussed.

Ultrahigh-Speed In-Memory Electronics Enabled by Proximity-Oxidation-Evolved Metal Oxide Redox Transistors

The pursuit of a universal device that combines nonvolatile multilevel storage, ultrafast writing/erasing speed, nondestructive readout, and embedded processing with low power consumption demands the development of innovative architectures. Although thin-film transistors and redox-based resistive-switching devices have independently been proven to be ideal building blocks for data processing and storage, it is still difficult to achieve both well-controlled multilevel memory and high-precision ultrafast processing in a single unit, even though this is essential for the large-scale hardware implementation of in-memory computing. In this work, an ultrafast (≈42 ns) and programable redox thin-film transistor (ReTFT) memory made of a proximity-oxidation-grown TiO₂ layer is developed, which has on/off ratio of 10⁵ , nonvolatile multilevel analog storage with a long retention time, strong durability, and high reliability. Utilizing the proof-of-concept ReTFTs, circuits capable of performing fundamental NOT, AND, and OR operations with reconfigurable logic-in-memory processing are developed. Further, on-demand signal memory-processing operations, like multi-terminal addressable memory, learning, pattern recognition, and classification, are explored for prospective application in neuromorphic hardware. This device, which operates on a fundamentally different mechanism, presents an alternate solution to the problems associated with the creation of high-performing in-memory processing technology.

Low-Energy-Consumption and Electret-Free Photosynaptic Transistor Utilizing Poly(3-hexylthiophene)-Based Conjugated Block Copolymers

Neuromorphic computation possesses the advantages of self-learning, highly parallel computation, and low energy consumption, and is of great promise to overcome the bottleneck of von Neumann computation. In this work, a series of poly(3-hexylthiophene) (P3HT)-based block copolymers (BCPs) with different coil segments, including polystyrene, poly(2-vinylpyridine) (P2VP), poly(2-vinylnaphthalene), and poly(butyl acrylate), are utilized in photosynaptic transistor to emulate paired-pulse facilitation, spike time/rate-dependent plasticity, short/long-term neuroplasticity, and learning-forgetting-relearning processes. P3HT serves as a carrier transport channel and a photogate, while the insulating coils with electrophilic groups are for charge trapping and preservation. Three main factors are unveiled to govern the properties of these P3HT-based BCPs: i) rigidity of the insulating coil, ii) energy levels between the constituent polymers, and iii) electrophilicity of the insulating coil. Accordingly, P3HT-b-P2VP-based photosynaptic transistor with a sought-after BCP combination demonstrates long-term memory behavior with current contrast up to 105 , short-term memory behavior with high paired-pulse facilitation ratio of 1.38, and an ultralow energy consumption of 0.56 fJ at an operating voltage of -0.0003 V. As far as it is known, this is the first work to utilize conjugated BCPs in an electret-free photosynaptic transistor showing great potential to the artificial intelligence technology.

Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing

Two-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.

Integrated In-Sensor Computing Optoelectronic Device for Environment-Adaptable Artificial Retina Perception Application

With the development and application of artificial intelligence, there is an appeal to the exploitation of various sensors and memories. As the most important perception of human beings, vision occupies more than 80% of all the received information. Inspired by biological eyes, an artificial retina based on 2D Janus MoSSe was fabricated, which could simulate functions of visual perception with electronic/ion and optical comodulation. Furthermore, inspired by human brain, sensing, memory, and neuromorphic computing functions were integrated on one device for multifunctional intelligent electronics, which was beneficial for scalability and high efficiency. Through the formation of faradic electric double layer (EDL) at the metal-oxide/electrolyte interfaces could realize synaptic weight changes. On the basis of the optoelectronic performances, light adaptation of biological eyes, preprocessing, and recognition of handwritten digits were implemented successfully. This work may provide a strategy for the future integrated sensing-memory-processing device for optoelectronic artificial retina perception application.

Perovskite photodetectors and their application in artificial photonic synapses

Organic-inorganic hybrid perovskites exhibit superior optoelectrical properties and have been widely used in photodetectors. Perovskite photodetectors with excellent detectivity have great potential for developing artificial photonic synapses which can merge data transmission and storage. They are highly desired for next generation neuromorphic computing. The recent progress of perovskite photodetectors and their application in artificial photonic synapses are summarized in this review. Firstly, the key performance parameters of photodetectors are briefly introduced. Secondly, the recent research progress of photodetectors including photoconductors, photodiodes, and phototransistors is summarized. Finally, the applications of perovskite photodetectors in artificial photonic synapses in recent years are highlighted. All these demonstrate the great potential of perovskite photonic synapses for the development of artificial intelligence.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

An Artificial Mechano-Nociceptor with Mott Transition

Intelligent touch sensing is now becoming an essential part of various human-machine interactions and communication, including in touchpads, autonomous vehicles, and smart robotics. Usually, sensing of physical objects is enabled by applied force/pressure sensors; however, reported conventional tactile devices are not able to differentiate sharp and blunt objects, although sharp objects can cause unavoidable damage. Therefore, it is central issue to implement electronic devices that can classify sense of touch and simultaneously generate pain signals to avoid further potential damage from sharp objects. Here, concept of force-enabled nociceptive behavior is proposed and demonstrated using vanadium oxide-based artificial receptors. Specifically, versatile criteria of bio-nociceptor like threshold, relaxation, no adaptation, allodynia, and hyperalgesia behaviors are triggered by pointed force, but the device does not mimic any of these by the force applied by blunt objects; thus, the proposed device classifies the intent of touch. Further, supported by finite element simulation, the nanoscale dynamic is unambiguously revealed by conductive atomic force microscopy and results are attributed to the point force-triggered Mott transition, as also confirmed by temperature-dependent measurements. The reported features open a new avenue for developing mechano-nociceptors, which enable a high-level of artificial intelligence within the device to classify physical touch.

Free-Standing Multilayer Molybdenum Disulfide Memristor for Brain-Inspired Neuromorphic Applications

Recently, atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDs) have attracted great interest in electronic and opto-electronic devices for high-integration-density applications such as data storage due to their small vertical dimension and high data storage capability. Here, we report a memristor based on free-standing multilayer molybdenum disulfide (MoS₂) with a high current on/off ratio of ∼10³ and a stable retention for at least 3000 s. Through light modulation of the carrier density in the suspended MoS₂ channel, the on/off ratio can be further increased to ∼10⁵. Moreover, the essential photosynaptic functions with short- and long-term memory (STM and LTM) behaviors are successfully mimicked by such devices. These results also indicate that STM can be transferred to LTM by increasing the light stimuli power, pulse duration, and number of pulses. The electrical measurements performed under vacuum and ambient air conditions propose that the observed resistive switching is due to adsorbed oxygen and water molecules on both sides of the MoS₂ channel. Thus, our free-standing 2D multilayer MoS₂-based memristors propose a simple approach for fabrication of a low-power-consumption and reliable resistive switching device for neuromorphic applications.

Artificial Synapses Based on Lead-Free Perovskite Floating-Gate Organic Field-Effect Transistors for Supervised and Unsupervised Learning

Synaptic devices are expected to overcome von Neumann's bottleneck and served as one of the foundations for future neuromorphic computing. Lead halide perovskites are considered as promising photoactive materials but limited by the toxicity of lead. Herein, lead-free perovskite CsBi₃I₁₀ is utilized as a photoactive material to fabricate organic synaptic transistors with a floating-gate structure for the first time. The devices can maintain the I_light/I_dark ratio of 10³ for 4 h and have excellent stability within the 30 days test even without encapsulation. Synaptic functions are successfully simulated. Notably, by combining the decent charge transport property of the organic semiconductor and the excellent photoelectronic property of CsBi₃I₁₀, synaptic performance can be realized even with an operating voltage as low as -0.01 V, which is rare among floating-gate synaptic transistors. Furthermore, artificial neural networks are constructed. We propose a new method that can simulate the synaptic weight value in multiple digit form to achieve complete gradient descent. The image recognition test exhibits thrilling recognition accuracy for both supervised (91%) and unsupervised (81%) classifications. These results demonstrate the great potential of floating-gate organic synaptic transistors in neuromorphic computing.

High ionic conductivity Li0.33La0.557TiO3nanofiber/polymer composite solid electrolyte for flexible transparent InZnO synaptic transistors

With the rapid development of wearable artificial intelligence devices, there is an increasing demand for flexible oxide neuromorphic transistors with the solid electrolytes. To achieve high-performance flexible synaptic transistors, the solid electrolytes should exhibit good mechanical bending characteristics and high ion conductivity. However, the polymer-based electrolytes with good mechanical bending characteristics show poor ion conductivity (10^-6-10^-7S cm^-1), which limits the performance of flexible synaptic transistors. Thus, it is urgent to improve the ion conductivity of the polymer-based electrolytes. In the work, a new strategy of electrospun Li_0.33La_0.557TiO₃nanofibers-enhanced ion transport pathway is proposed to simultaneously improve the mechanical bending and ion conductivity of polyethylene oxide/polyvinylpyrrolidone-based solid electrolytes. The flexible InZnO synaptic transistors with Li_0.33La_0.557TiO₃nanofibers-based solid electrolytes successfully simulated excitatory post-synaptic current, paired-pulse-facilitation, dynamic time filter, nonlinear summation, two-terminal input dynamic integration and logic function. This work is a useful attempt to develop high-performance synaptic transistors.

Optoelectronic Artificial Synapses Based on Two-Dimensional Transitional-Metal Trichalcogenide

The memristor is a foundational device for an artificial synapse, which is essential to realize next-generation neuromorphic computing. Herein, an optoelectronic memristor based on a two-dimensional (2D) transitional-metal trichalcogenide (TMTC) is designed and demonstrated. Owing to the excellent optical and electrical characteristics of titanium trisulfide (TiS₃), the memristor exhibits stable bipolar resistance switching (RS) as a result of the controllable formation and rupturing of the conductive aluminum filaments. Multilevel storage is realized with light of multiple wavelengths between 400 and 808 nm, and the synaptic properties such as conduction modulation and spiking timing-dependent plasticity (STDP) are achieved. On the basis of the photonic potentiation and electrical habitual ability, Pavlovian-associative learning is successfully established on this TiS₃-based artificial synapse. All these results reveal the large potential of 2D TMTCs in artificial neuromorphic chips.

IGZO/CsPbBr3-Nanoparticles/IGZO Neuromorphic Phototransistors and Their Optoelectronic Coupling Applications

Optoelectronic synaptic devices are of great scientific and practical importance because of various potential applications such as ocular simulating and optical-electrical managers based on a new optoelectronic coupling mechanism. In this work, we design a novel channel layer with p-type CsPbBr₃ nanoparticles (NPs) buried in an InGaZnO (IGZO) film to construct the corresponding thin-film transistors (TFTs), which exhibits intense improvement in visible-light photosensitivity and synaptic plasticity as compared to the pure IGZO counterpart. Specifically, the composite device is able to exhibit versatile synaptic behavior under light stimuli with density as low as 0.12 μW/cm² and with the gain 5-20 times higher than that of the IGZO TFT in the visible-light region. Based on the band alignment between the IGZO and NPs, the excitation and decay processes of intrinsic and photoinduced carriers are discussed. Moreover, owing to the gate bias control in a three-terminal configuration, our TFT synapses can imitate complex biological behaviors including the famous "Pavlov's dog" experiment and the "reward and punishment mechanism" of the brain via editing the gate voltage/light pulse stimuli.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Memristive Artificial Synapses for Neuromorphic Computing

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

Single-Crystalline All-Oxide α-γ-β Heterostructures for Deep-Ultraviolet Photodetection

Recent advancements in gallium oxide (Ga₂O₃)-based heterostructures have allowed optoelectronic devices to be used extensively in the fields of power electronics and deep-ultraviolet photodetection. While most previous research has involved realizing single-crystalline Ga₂O₃ layers on native substrates for high conductivity and visible-light transparency, presented and investigated herein is a single-crystalline β-Ga₂O₃ layer grown on an α-Al₂O₃ substrate through an interfacial γ-In₂O₃ layer. The single-crystalline transparent conductive oxide layer made of wafer-scalable γ-In₂O₃ provides high carrier transport, visible-light transparency, and antioxidation properties that are critical for realizing vertically oriented heterostructures for transparent oxide photonic platforms. Physical characterization based on X-ray diffraction and high-resolution transmission electron microscopy imaging confirms the single-crystalline nature of the grown films and the crystallographic orientation relationships among the monoclinic β-Ga₂O₃, cubic γ-In₂O₃, and trigonal α-Al₂O₃, while the elemental composition and sharp interfaces across the heterostructure are confirmed by Rutherford backscattering spectrometry. Furthermore, the energy-band offsets are determined by X-ray photoelectron spectroscopy at the β-Ga₂O₃/γ-In₂O₃ interface, elucidating a type-II heterojunction with conduction- and valence-band offsets of 0.16 and 1.38 eV, respectively. Based on the single-crystalline β-Ga₂O₃/γ-In₂O₃/α-Al₂O₃ all-oxide heterostructure, a vertically oriented DUV photodetector is fabricated that exhibits a high photoresponsivity of 94.3 A/W, an external quantum efficiency of 4.6 × 10⁴%, and a specific detectivity of 3.09 × 10¹² Jones at 250 nm. The present demonstration lays a strong foundation for and paves the way to future all-oxide-based transparent photonic platforms.

Violet-light stimulated synaptic and learning functions in a zinc-tin oxide photoelectric transistor for neuromorphic computation

In contrast to the commonly present UV light-stimulated synaptic oxide thin-film transistors, this study demonstrates a violet light (wavelength of 405 nm) stimulated zinc-tin oxide (ZTO) photoelectric transistor for potential application in optical neuromorphic computation. Owing to the light-induced oxygen vacancy ionization and persistent photoconductivity effect in ZTO, this device well imitates prominent synaptic functions, including photonic potentiation, electric depression, and short-term memory (STM) to long-term memory (LTM) transition. A highly linear and broad dynamic range of photonic potentiation can be achieved by modulating the light power density, while electric depression is realized by gate voltage pulsing. In addition, the brain-like re-learning experience with extended forgetting time (200 s) is well mimicked by the ZTO photoelectric transistor. As a result, the ZTO photoelectric transistor provides excessive synaptic function with multi-series of synaptic weight levels (90 levels for each given light power density), which makes it prevalent in the neuromorphic computation of massive data as well as in learning-driven artificial intelligence computation.

Investigation of hysteresis in hole transport layer free metal halide perovskites cells under dark conditions

Recent research is a testimony to the fact that perovskite material based solar cells are most efficient since they exhibit high power conversion efficiency and low cost of fabrication. Various perovskite materials display hysteresis in their current-voltage characteristic which accounts for memory behaviour. In this paper, we demonstrate efficient non-volatile memory devices based on hybrid organic-inorganic perovskite (CH₃NH₃PbI₃) as a resistive switching layer on a Glass/Indium Tin Oxide (ITO) substrate. Our perovskite solar cells have been developed over the fully solution processed electron transport layer (ETL) which is a combination of SnO₂ and mesoporous (m)-TiO₂ scaffold layers. Hysteresis behaviour was observed in the current-voltage analysis achieving high ratio of ON & OFF current under dark and ambient conditions. Proposed perovskite-based Glass/ITO/SnO₂/m-TiO₂/CH₃NH₃PbI₃/Au device has a hole transport layer (HTL) free structure, which is mainly responsible for a large ratio of ON & OFF current. The presence of voids in the scaffold m-TiO₂ layer are also accountable for increasing electron/hole path length which escalates the recombination rate at the surface of the ETL/perovskite interface resulting in large hysteresis in the I-V curve. This memristor device operates at a low energy due to SnO₂ layer's higher electron mobility and wide energy band gap. Our experimental results also show the dependency of voltage scan range & rate of scanning on the hysteresis behaviour in dark conditions. This memristive behaviour of the proposed device depicts drift in hysteresis loop with respect to the number of cycles, which would have a significant impact in neuromorphic applications. Moreover, due to the identical fabrication process of the proposed perovskite-based memristor device and perovskite solar cells, this device could be integrated inside a photovoltaic array to work as a power-on-chip device, where generation and computation could be possible on the same substrate for memory and neuromorphic applications.

Environment-Adaptable Photonic-Electronic-Coupled Neuromorphic Angular Visual System

Environment-adaptable photonic-electronic-coupled devices can help overcome major challenges related to the extraction of highly specific angular information, such as human visual perception. However, a true implementation of such a device has rarely been investigated thus far. Herein, we provide an approach and demonstrate a proof-of-concept solid-state semiconductor-based highly transparent, optical-electrical-coupled, self-adaptive angular visual perception system that can fulfill the versatile criteria of the human vision system. Specifically, all of the primitive functions of visual perception, such as broad angular sensing, processing, and manifold memory storage, are demonstrated and comodulated using optical and electric pulses. This development represents an essential step forward in the fabrication of an environment-adaptable artificial angular perception framework with deep implications in the fields of optoelectronics, artificial eyes, and memory storage applications.

Nano-engineering and functionalization of hybrid Au-MexOy-TiO2 (Me = W, Ga) hetero-interfaces for optoelectronic receptors and nociceptors

Bio-inspired nano-electronic devices are key instruments for the development of advanced artificial intelligence systems, which will shape the future of humanoid nano-robotics. An emerging demand is realized for an accurate reception of environmental stimuli via visual perception, processing and realization of optical signals. The present study demonstrates the capability of functionalized all-oxide heterostructured two-dimensional (2D) plasmonic devices for the self-adaptive recognition of visual optical pulses. Specifically, the nano-engineering of the metal/semiconductor interface and co-modulation of heterostructured 2D semiconductor hetero-interfaces of Au/WO3 : TiO2 and Au/Ga2O3 : TiO2 facilitated the receptive and nociceptive detection of visible light pulses. A decrease in the dark current of the Au/WO3 : TiO2 unit resulted in the development of sensitive visible light photoreceptors. Furthermore, the modulation of charge transfers at the Au/Ga2O3 : TiO2 hetero-interfaces were the key parameter to determine the optical reception characteristics and nociceptive performance of all-oxide optoelectronic devices. Specifically, the rapid thermal annealing (RTA) of 2D Ga2O3 in N2 atmosphere ensured the modulation of charge transfer at Au/Ga2O3 : TiO2 hetero-interfaces in plasmonic devices. Thus, hetero-interface engineering enabled the effective control of charge transfer at 2D hetero-interfaces for an adaptive perception of visible optical pulses. Consequently, the fabricated sensitive Au/Ga2O3 (N2) : TiO2 bio-inspired unit emulated the optical functionalities of corneal nociceptors.