For the conventional von Neumann based vision systems, the sensing, memory, and processing units are separated. Shuttling of redundant data between separated image sensing, memory, and processing units causes a high latency and energy consumption. To break these limitations, the next-generation neuromorphic visual systems, which integrate light information sensing, memory, and processing, can reduce the data transfer, thus improving their time and energy efficiencies. As the basis of the hardware-implementing of neuromorphic visual systems, optoelectronic artificial synapse devices have been extensively investigated in recent years. By integrating the functions of synaptic devices and light-sensing elements, the optoelectronic artificial synapse devices pave the way for constructing new neuromorphic vision systems with low latency, high energy efficiency and good reliability. Many materials are widely utilized for optoelectronic artificial synapse devices, and operation mechanisms of the present optoelectronic artificial synapse devices mainly include the ionization and dissociation of oxygen vacancy, the trapping/detrapping of photogenerated carriers, the light-induced phase change, and the interaction between light and ferroelectric materials. In this short review, the recent progresses in optoelectronic artificial synapse devices are introduced from the perspectives of their operation mechanisms. Besides, advantages and challenges of the devices are analyzed from the view of operation mechanisms. Finally, the advanced prospect and research aspect of optoelectronic artificial synapse devices are outlined for the application.

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

Brain-inspired neuromorphic computing refers to simulation of the structure and functionality of the human brain via the integration of electronic or photonic devices. Artificial synapses are the most abundant computation element in the brain-inspired system. Memristors are considered to be ideal devices for artificial synapse applications because of their high scalability and low power consumption. Based on Ohm’s law and Kirchhoff’s law, memristor crossbar arrays can perform parallel multiply-accumulate operations in situ, leading to analogue computing with greatly improved speed and energy efficiency. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with CMOS processes. This work reviews the research progress of oxide memristors for brain-inspired computing, mainly focusing on their resistance switching mechanisms, device structures and performances. These devices fall into three categories: electrical memristors, memristors controlled via both electrical and optical stimuli, and all-optically controlled memristors. The working mechanisms of electrical memristors are commonly related to microstructure change and Joule heat that are detrimental to device stability. The device performance can be improved by optimizing device structure and material composition. Tuning the device conductance with optical signals can avoid microstructure change and Joule heat as well as reducing energy consumption, thus making it possible to address the stability problem. In addition, optically controlled memristors can directly response to external light stimulus enabling integrated sensing-computing-memoring within single devices, which are expected to be used for developing next-generation vision sensors. Hence, the realization of all-optically controlled memristors opens a new window for research and applications of memristors.

Memristor, fusing the functions of storage and computing within a single device, is one of the core electronic components to solve the bottleneck of von Neumann architecture. With the unique volatile/non-volatile resistive switching characteristic, memristor can simulate the function of synapses/neurons in brain well. In addition, due to the compatibility with traditional complementary metal-oxide-semiconductor (CMOS) processes, metal-oxide-based memristors have received a lot of attention. In recent years, many kinds of metal-oxide memristors based on single dielectric layer have been proposed. However, there are still some problems such as the instability of switching voltage, fluctuation of high/low resistance state and poor endurance of memristive device. Thus, the researchers have successfully optimized the device performance by introducing the double dielectric layer into the metal-oxide memristors. In this article, we introduce the advantages of double dielectric layers-based metal-oxide memristors, and discuss their mechanism and design of double dielectric layers-based metal-oxide memristors. Eventually, we introduce their potential applications in neuromorphic computing. This review provides some enlightenment on how to design high-performance metal-oxide memristor based on double dielectric layers.

Development of novel artificial synaptic devices, which make up the majority of neural networks, has emerged as a pivotal path to hardware realization of neuromorphic computing. An electrochemical ion synapse, also known as a three-terminal synaptic device based on electrochemical transistors, is a device that may efficiently use ions in the electrolyte layer to modify channel conductivity. By electrochemical doping and recovering ions in channel materials exhibiting redox activity, this device mimics biological synaptic properties. The advantages of the electrochemical ion synapse, which uses proton (H⁺) as the doping particle, are lower energy consumption, faster operation, and a longer cycle life among the ions that alter the channel material's conductance. This article reviews the recent research progress on proton-regulated electrochemical ion synapses, summarizes the material systems used for the channel layer and electrolyte layer of proton-regulated electrochemical ion synapses, analyzes the challenges faced by proton-regulated electrochemical ion synapses, and points out directions on their future development.

As the basic and essential unit of neuromorphic computing system, artificial synaptic devices exhibit great potential in accelerating the high-performance parallel computation, artificial intelligence, and adaptive learning. Among them, electrolyte-gated synaptic transistors (EGSTs) have received increasing attention as the next generation neuromorphic devices owing to its controllable channel conductance. The devices exhibit the abilities of simulating the short-term plasticity (STP) and long-term plasticity (LTP) of the neural synapses. However, most of EGSTs exhibit short persistence for LTP and their channel conductance is difficult to be adjusted due to the rapid self-discharge of the electric double layer. In this work, the EGSTs based on water-induced In₂O₃ as the channel and chitosan as gate electrolyte were constructed and the O₂ plasma treatments were performed. The formation of traps on the channel surface is caused by the O₂ plasma treatments, which leads to capturing hydrogen ions at interface of the electrolyte/channel layer, and the device performance exhibits an enlarged hysteresis window, so as to regulate LTP of EGSTs. Biological synaptic functions, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), STP, and LTP, were mimicked by electrochemical doping and electrostatic coupling effects. Meanwhile, based on the experimentally verified potentiation/depression characteristics of the EGSTs, a three-layer artificial neural network is applied for handwritten digit recognition, and simulation tests can obtain high recognition accuracy of 94.7%. These results reveal that surface plasma treatment is one of the key technologies to affect the device performance, which has great potential in regulating synaptic function of EGSTs.

Multistage pain perception is of great significance for surviving the outside harmful stimuli for organisms. In this work, using a sodium alginate biopolymer electrolyte as neurotransmitter layer, a 5×5 array of junctionless transistoris successfully fabricated for pain perception. The device operates well at low voltage (2 V) with a large current on-off ratio (>10⁴) and on-state current (>10 μA). This coplanar-gate array can not only emulate the important functions of synapses, such as excitatory postsynaptic current, paired-pulse facilitation, and dynamic filtering, but also successfully mimic pain-perception and sensitization abilities of the artificial nociceptor network. Furthermore, this work also successfully emulates the multistage spatio-temporal sensitization in the nociceptor network. Construction of this kind of network system provides a new way for the application of the next-generation neuromorphic brain-like system.

A memristor with analogue resistive switching (RS) memory behaviors could provide enough conductance states for high-efficiency neuromorphic computing because this type RS memory feature can avoid conductance clamping, steeply change, and computing invalidation. Simulating the behavior of biological synapses under stimulus pulse can better reveal the bionic characteristic mechanism of electronic devices and provide support for high performance neuromorphic computation. Synaptic paired-pulse facilitation (PPF) is an important characteristic of biological synapses, reflecting the facilitation and adaptation process under external stimuli, which is crucial to reveal the working mechanism of neurons. A memristor with the structure of the Ag/FeO_x/ITO was prepared by RF magnetron sputtering, which was designed by energy band engineering for the PPF demonstration. Experimental measurement of the electric properties illustrates that the developed memristor displays an excellent asymptotic nonlinear resistance switching behaviors, which is so called analogue RS memory behavior. Importantly, this developed memristor presents this analogue RS memory behavior during 3000 I-V sweepings, provides dissociable 16 conductance states that could be well maintained for 10⁴ s, illustrating that these available conductance states are nonvolatile. Based on the energy band structure and oxygen vacancy (V_O) defects, a physical mechanism, which involved trap sites softly filled by the injection electron, electron tunneling between the potential barrier built by the contact of Ag/FeO_x and FeO_x/ITO, and the V_O migration that accompanied a volatile feature to some extent, is proposed to comprehend the observed analogue RS memory behaviors. According to this mechanism, a typical PPF feature is obtained after modulating the voltage pulse width and amplitude. The observed analogue RS memory behaviors and PPF behaviors show a promising potential and advantage in neuromorphic computing.

Mimicking of brain perceptual processing mode is of great importance for the design of bionic intelligent perceptual system. On the meantime, adopting functional materials with biocompatibility and biodegradability to construct environment-friendly neuromorphic devices is also an important aspect for synaptic electronics. Here, gelatin/carboxylated chitosan (GEL/C-CS) composite electrolyte film was adopted as gate dielectrics in oxide neuromorphic transistors. Synaptic plasticities, including excitory post synaptic current and paired pulse facilitation, were mimicked on the oxide neuromorphic transistor under different humidities. A quantitative processing method for tactile recognition of objects was proposed based on the spike number dependent synaptic plasticity. An artificial neural network was built in further. Recognition accuracy of MNIST handwritten digits is above 90%. Data from above evaluation show that the proposed GEL/C-CS gated neuromorphic device has a promising application potential in the design of bionic intelligent perceptual systems and brain inspired neuromorphic systems.

Relaxor ferroelectrics exhibit extensive applications in sensing technology, optoelectronics, high-density memory storage, and neuromorphic computing, owing to their superior dielectric and piezoelectric characteristics. However, conventional methods, including the Sawyer-Tower circuit and the positive-up-negative-down (PUND) pulse train, prove inadequate for nanoscale ultra-thin films, since the relaxor characteristics may be hindered by substantial leakage currents. In this study, a piezoresponse force microscopy (PFM)-based method for characterizing nanoscale relaxor properties was proposed. Taking ultra-thin Pb(Mg,Nb)O₃-PbTiO₃ (PMN-PT) films as examples, this work compares polarization hysteresis behavior under On-field and Off-field modes of the dual AC resonance tracking (DART) PFM measurements between relaxor PMN-PT and ferroelectric Pb(Zr,Ti)O₃ (PZT) thin films with varying thicknesses. Relaxor characteristics of nanometer-thick PMN-PT films are characterized by modulating amplitude of AC readout to eliminate potential false signals. Furthermore, PFM characterizations of PMN-PT ultra-thin films under different in-plane compressive strains and thicknesses demonstrate that the relaxor characteristics are suppressed and ferroelectric properties are observed at relatively large compressive strains of 3.19%. Additionally, the critical thickness for ferroelectric-relaxor transition is identified. These results verify availability of the proposed PFM-based method for characterizing nanoscale relaxor properties. Therefore, this study not only provides a novel characterization method for exploration of the relaxor in ultra-thin films, but also establishes a foundation for understanding the relaxor polarization behavior in ferroelectric materials, thereby advancing applications of relaxor ferroelectric materials in low-dimensional electronic devices.

In the process of modern electronic devices developing towards miniaturization, integration, and multi-functional intelligence, two-dimensional (2D) materials offer a promising development path for this field with their diverse structures and unique physicochemical properties. Among numerous 2D materials, Bi₂O₂Se has attracted extensive attention due to its suitable bandgap, high carrier mobility, and excellent environmental stability. However, current Bi₂O₂Se-based devices still suffer from issues such as large dark current and low responsivity, which hinder their further development in the field of high-performance optoelectronic devices. In this study, high-quality 2D Bi₂O₂Se nanosheets were grown on mica substrates via chemical vapor deposition. Innovatively, symmetric graphene (Gr) electrodes were used to construct a Gr/Bi₂O₂Se/Gr bi-heterojunction device. This structure utilizes the built-in electric field formed at the dual interfaces between Gr and Bi₂O₂Se to optimize carrier injection and separation processes. Subsequently, the current-voltage characteristics, transient current responses, and spectral responsivity of the device under dark and illumination were systematically characterized at different wavelengths. Especially, the dynamic electrical behavior of the device under pulsed light stimulation was thoroughly investigated to mimic short-term and long-term synaptic plasticity functions. Under 532 nm light illumination, the device exhibits a favorable responsivity of 2.52 A/W and a detectivity of 3.39×10⁹ Jones, and maintains stable photoresponse across a wide wavelength range (365-1050 nm), confirming its potential as a broadband photodetector. Especially under 365 nm pulsed stimulation, the device demonstrates the transition from short-term plasticity to long-term plasticity. By adjusting the intensity, frequency, and number of light pulses, key biological synaptic behaviors, including excitatory postsynaptic currents and spike-timing-dependent plasticity, were accurately simulated. Furthermore, the device successfully reproduces the feature of “empirical learning”, fully demonstrating its potential in the field of neuromorphic computing.