Synaptic performanceof SnO2: Ag-based optoelectronicdevices

Optoelectronic synaptic devices outperform electrical synapses in speed, energy efficiency, and integration density. Recent progress in visual sensing and optogenetics has led to the integration of light-sensitive materials in these devices, promising unmatched speed, connectivity, and bandwidth. Here, we present a copper phthalocyanine (CuPc) based optoelectronic synaptic device boasting femto Joule power consumption stable at room temperature. The optoelectronic synapse can be operated with energy consumption as low as 430.4 fJ which is very attractive from the point of view of low-power neuromorphic devices. By modulating light pulses, the neuromorphic behavior can be emulated including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), transitioning from short-term plasticity (STP) to long-term plasticity (LTP), spike-rate dependent plasticity (SRDP) and spike-number dependent plasticity (SNDP), etc. Optical potentiation and electrical depression are observed with combined optical and electrical stimulation, proving the multi-functionality of the synapse. Furthermore, the device demonstrates classical associative learning behaviors like Pavlovian conditioning using optical and electrical stimuli. We have established the pain conditioning processes such as hyperalgesic response and pain extinction effects with varying optical pulse amplitudes. These results render the CuPc-based devices as multifunctional and highly versatile artificial synaptic devices for future computing applications, offering unprecedented efficiency and functionality in neuromorphic systems.

Zero-power optoelectronic synaptic devices

An oxide-based heterojunction optoelectronic synaptic device with wideband and rapid response performance

Current artificial neuromorphic systems are mainly constructed using solid‐state optoelectronic synaptic devices, limiting their potential artificial intelligence applications in liquid medium. Here, colloidal CuInGaSe/ZnSe quantum dots (QDs) with environment‐benign feature and broad visible light absorption are prepared to fabricate a photoelectrochemical (PEC)‐type optoelectronic synaptic device operating in aqueous electrolyte, enabling unique biological synaptic behaviors including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP) and learning‐forgetting‐relearning process when simulated by optical pulses with various wavelength, pulse number, frequency, power density, and applied voltage. These results demonstrate the feasibility of building PEC‐type optoelectronic synapses using colloidal QDs, paving the way to realize available optoelectronic synaptic devices for prospective underwater artificial intelligence technology.

Electroluminescent synaptic devices with logic functions

Simulating biological synaptic functionalities through artificial synaptic devices opens up an innovative way to overcome the von Neumann bottleneck at the device level. Artificial optoelectronic synapses provide a non‐contact method to operate the devices and overcome the shortcomings of electrical synaptic devices. With the advantages of high photoelectric conversion efficiency, adjustable light absorption coefficient, and broad spectral range, nanowires (NWs)‐based optoelectronic synapses have attracted wide attention. Herein, to better promote the applications of nanowires‐based optoelectronic synapses for future neuromorphic systems, the functionalities of optoelectronic synaptic devices and the current progress of NWs optoelectronic synaptic devices in UV–vis–IR spectral range are introduced. Furthermore, a bridge between NWs‐based optoelectronic synaptic device and the neuromorphic system is established. Challenges for the forthcoming development of NWs optoelectronic synapses are also discussed. This review may offer a vision into the design and neuromorphic applications of NWs‐based optoelectronic synaptic devices.

Here, we present the synaptic characteristics of AlN-based conductive bridge random access memory (CBRAM) as a synaptic device for neuromorphic systems. Both non-volatile and volatile memory are observed by simply controlling the strength of the Cu filament inside the AlN film. For non-volatile switching induced by high compliance current (CC), good retention with a strong Cu metallic filament is verified. Low-resistance state (LRS) and high-resistance state (HRS) conduction follow metallic Ohmic and trap-assisted tunneling (TAT), respectively, which are supported by I–V fitting and temperature dependence. The transition from long-term plasticity (LTP) to short-term plasticity (STP) is demonstrated by increasing the pulse interval time for synaptic device application. Also, paired-pulse facilitation (PPF) in the nervous system is mimicked by sending two identical pulses to the CBRAM device to induce STP. Finally, potentiation and depression are achieved by gradually increasing the set and reset voltage in pulse transient mode.

Filamentary and interface switching of CMOS-compatible Ta2O5 memristor for non-volatile memory and synaptic devices

Optical artificial synapses possess several advantages, including high bandwidth, strong interference immunity, and ultra-fast signal transmission, overcoming the limitations of electrically stimulated synapses. Among various functional materials, 2D materials exhibit exceptional optical and electrical properties. By utilizing van der Waals heterostructures formed by these materials through rational design, synaptic devices can mimic the information perception ability of biological systems. This lays the foundation for low-energy artificial vision systems and neuromorphic computing. This study introduces an inhibitory artificial synapse based on photoelectric co-modulation of graphene/WSe2 van der Waals heterojunctions. By synergistically applying gate voltage and light pulses, we simulate memory and logic functions observed in the brain’s visual cortex. We achieve the construction of inhibitory synapses, enabling properties such as postsynaptic current response, short-term and long-term plasticity, and paired-pulse facilitation. Additionally, we accomplish the inverse recovery of device conductivity through separate gate voltage stimulation. Through bidirectional modulation of the artificial synaptic conductance, we construct an artificial hardware neural network that achieves 92.5% accuracy in recognizing handwritten digital images from the MNIST dataset. The network also has good recognition accuracy for handwritten digital images with different standard deviation Gaussian noise applied and other datasets. Furthermore, we successfully mimic the neural behavior of aversive learning for alcohol withdrawal in alcoholic patients using the device properties. The promising capabilities of artificial synapses constructed through electrical and optical synergistic modulation make them suitable for wearable electronics and artificial vision systems.

Neuromorphic computing systems are based on new architecture inspired by biological learning behavior and biological structure to overcome the shortcomings of the von Neumann system, such as high energy consumption and generation of excessive redundant data. However, common synaptic‐device designs are still need to carry an external power source, which is detrimental to both the miniaturization of smart devices and the further reduction of energy consumption. Therefore, self‐powered optoelectronic synapses hold great promise for achieving energy‐efficient artificial intelligence applications. Given the advantages of heterogeneous integration and ferroelectric performance, the 2D ferroelectric In2Se3 has become a typical candidate material for biological synaptic devices and neuromorphic computing. Although, various structures of ferroelectric synaptic devices are developed, most of which are controlled by optical or electrical pulses, hardly any reports discuss the operation in self‐powered mode. In this work, an In2Se3/MoS2 ferroelectric lateral heterojunction‐type optoelectronic synaptic device is fabricated to mimic biological synaptic functions in voltage mode or self‐driven mode. In addition, two simplified image pre‐processing methods based on device arrays are shown to improve the recognition accuracy of a back‐end neural network. The proposed approach suggests a new route for designing and applying high‐performance synaptic devices.

Xanthan gum biopolymer gated low voltage-operating photo synaptic organic field-effect transistor for neuromorphic electronics

A three-terminal synaptic transistor enables more accurate controllability over the conductance compared with traditional two-terminal synaptic devices for the synaptic devices in hardware-oriented neuromorphic systems. In this work, we fabricated IGZO-based three-terminal devices comprising HfAlOx and CeOx layers to demonstrate the synaptic operations. The chemical compositions and thicknesses of the devices were verified by transmission electron microscopy and energy dispersive spectroscopy in cooperation. The excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), short-term potentiation (STP), and short-term depression (STD) of the synaptic devices were realized for the short-term memory behaviors. The IGZO-based three-terminal synaptic transistor could thus be controlled appropriately by the amplitude, width, and interval time of the pulses for implementing the neuromorphic systems.

While biological vision systems excel at in-memory processing with low power consumption, traditional silicon-based vision chips struggle with high energy demands. This gap motivates the exploration of alternative materials for artificial intelligence applications. This paper presents a VOx-based optoelectronic synaptic memristive device. The proposed artificial synaptic device ITO/VO x /Pt mimics biological functions such as potentiation (P), depression (D), long-term memory, short-term memory (STM), and paired-pulse facilitation (PPF). The PPF index, standing at 105%, suggests a favorable pattern in STM function. The device served as synapses within a spiking neural network showing an achievable pattern classification accuracy of 88.68%, highlighting the potential of the VO x synaptic device for pattern classification tasks. The suggested VO x -based synaptic devices could represent an efficient pattern recognition and visual perception application.

Optoelectronic synapses use optical signals as modulation inputs and offer advantages such as low-power consumption, non-contact operation, and high parallelism. However, devices operating in the visible and near-infrared regions are susceptible to ambient light interference, leading to signal distortion and high background noise. In contrast, optoelectronic synaptic devices working in a solar-blind region are impervious to external ambient interference, enabling them to achieve stable and high-fidelity signal transmission effectively. Ga2O3 as a wide bandgap with inherent oxygen-vacancy defects has strong deep-ultraviolet light sensitivity, making it an ideal material for solar-blind optoelectronic synapses. Here, high-performance solar-blind synaptic devices were prepared using nanowire network interfacial effects and nitrogen doping to modulate oxygen vacancies. Notably, the paired-pulse facilitation index increased by approximately 1.2 fold after doping. In handwritten digit recognition experiments, the recognition accuracy of the doped device exceeded 90%, representing an improvement of nearly 15% compared to the undoped counterpart. Furthermore, leveraging its enhanced light perception and strong memory characteristics, a precise optical encryption communication scheme with strong anti-interference capability was also designed. The optoelectronic synaptic devices based on element regulated Ga2O3 nanowire network provide a new way for the development of the next generation of high-performance solar-blind optoelectronic neuromorphic systems.

Artificial neural networks built with optoelectronic synaptic devices have been proven to process visual information effectively. However, it takes great latency time and energy consumption, especially facing dynamic visual information, due to the separated optical sensor, memory, and process unit. Reservoir computing (RC) based on optoelectronic synaptic devices provides an in-sensor RC for processing temporal information efficiently. It achieves efficient computation by sensing and processing optical signals directly with optoelectronic synaptic devices. Optoelectronic synaptic devices shine in visual information processing, whose application in visual sensing and processing will provide a viable hardware solution for in-sensor computing. Therefore, the application of optoelectronic synaptic devices in reservoir computing has prompted increasing attention. Herein, for promoting the application of physical reservoir computing (PRC) with optoelectrical synapses in machine vision, synaptic plasticity will be introduced first in this work and then illustrate the basic functions of optoelectronic synapses as well as their application in reservoir computing further, and provide a perspective on PRC with optoelectronic synapses in the final.