Low-power memristors based on layered 2D SnSe/graphene materials

This article demonstrates fabrication and testing of novel memory devices using indium sulfide (In2S3) as an active material. A wafer scalable process is used to develop nanostructured In2S3 for the mass production of optical memory devices. The developed devices have shown outstanding light-sensing capability as well as memory functionality. The In2S3 devices are highly responsive toward the 880 nm with higher responsivity and detectivity of 15.88 A/W and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.84\times 10^{{11}}$ </tex-math></inline-formula> Jones, respectively. In addition, these optical memory devices were able to mimic cognitive functionalities like high paired-pulse facilitation (PPF) index, long-term plasticity (LTP), and short-term plasticity (STP) compared to the state of the art. The optical memory devices have shown a higher PPF index of 227% and long-term memory (LTM) retention capability of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 10^{{3}}$ </tex-math></inline-formula> s. Devices were optically potentiated for broadband spectrum and reset using electrical stimulation. Moreover, In2S3-based devices have highly repeatable, scalable, stable, and reproducible characteristics at room temperature with a rise/fall time of 0.4/0.01 s. The fabricated optical memory devices can be deployed for future neuromorphic computing applications.

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.

Various hippocampal and neocortical synapses of mammalian brain show both short-term plasticity and long-term plasticity, which are considered to underlie learning and memory by the brain. According to Hebb’s postulate, synaptic plasticity encodes memory traces of past experiences into cell assemblies in cortical circuits. However, it remains unclear how the various forms of long-term and short-term synaptic plasticity cooperatively create and reorganize such cell assemblies. Here, we investigate the mechanism in which the three forms of synaptic plasticity known in cortical circuits, i.e., spike-timing-dependent plasticity (STDP), short-term depression (STD) and homeostatic plasticity, cooperatively generate, retain and reorganize cell assemblies in a recurrent neuronal network model. We show that multiple cell assemblies generated by external stimuli can survive noisy spontaneous network activity for an adequate range of the strength of STD. Furthermore, our model predicts that a symmetric temporal window of STDP, such as observed in dopaminergic modulations on hippocampal neurons, is crucial for the retention and integration of multiple cell assemblies. These results may have implications for the understanding of cortical memory processes.

Artificial photoelectric synaptic devices have exhibited remarkable advantages of low power consumption and high speed for neuromorphic computing. By integrating photodetection and memory functions, these devices offer a promising framework to resolve the limitations of von Neumann architectures. Herein, transparent photoelectric synaptic devices were fabricated based on amorphous InHfZnO/Ga2O3 heterojunction by plasma-enhanced atomic layer deposition. The heterojunction was comprehensively characterized with various techniques. For the fabricated devices, critical synaptic characteristics, including short-term plasticity (STP), paired-pulse facilitation (PPF), and long-term plasticity (LTP), were systematically characterized and evaluated under various optical conditions and temperatures. Based on temperature-dependent performance variations, the temperature-tunable mechanism of the devices was analyzed in terms of oxygen vacancy concentration evolution.

Neuromorphic computing offers a promising approach to overcoming the limitations of von Neumann architecture by mimicking biological synapses. While optoelectronic synapses have demonstrated synaptic plasticity through optical and electrical stimuli, most studies rely on ambient light conditions, limiting their robustness and functional complexity. Here, a WSe2/h-BN/SiO2 heterostructure-based optoelectronic synapse is presented that achieves precise synaptic weight modulation through co-stimuli of electrical and optical pulses. The device exhibits enhanced paired-pulse facilitation (PPF) and long-term plasticity (LTP/LTD), demonstrating stable and linear synaptic behavior. Notably, the study systematically analyzes the effects of co-stimuli firing conditions, revealing that both the intensity of light and voltage magnitude influence synaptic weight updates. The device achieves outstanding nonlinearity, high Gmax/Gmin, and stable depression recovery, essential for high-performance neuromorphic computing. Furthermore, ANN-based cognitive simulations using MNIST digits validate their potential for inference tasks, demonstrating near-ideal accuracy. These findings underscore the potential of co-stimuli-driven synapses for multi-modal cognitive systems, paving the way for advanced neuromorphic architectures beyond single-species stimuli constraints.

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

Synapse is one of the key components of brain nervous system. As the fourth electronic component, memristor can be used to act as synaptic device and construct bionic neural network, in which the synapses can be stimulated by either electrical excitation or optical irradiation. Up to now, there have been many researches on the optoelectronic synaptic devices and the main functional materials include binary oxides, perovskite complex oxides, solid electrolytes, organic and polymer materials, etc. Here, we report anoptoelectronic synaptic device based on SnO₂monolayer embedded with Ag nanocrystals and structured as ITO/SnO₂:Ag/p++-Si. It is found that the SnO2:Ag thin film can effectively absorb light illumination in the broadband range of 450 nm to 635 nm, and that the device has been successfully simulated with multiple synaptic behaviors in the broadband range, including excitatory postsynaptic current (EPSC), paired pulse facilitation (PPF) and the transition from short-term plasticity (STP) to long-term plasticity (LTP). Our newly developed optoelectronic synapse device shows great application potential in neuromorphological computing.

A new type artificial synapse based on the organic copolymer memcapacitor

SrFeOx (SFO) offers a topotactic phase transformation between an insulating brownmillerite SrFeO2.5 (BM-SFO) phase and a conductive perovskite SrFeO3 (PV-SFO) phase, making it a competitive candidate for use in resistive memory and neuromorphic computing. However, most of existing SFO-based memristors are nonvolatile devices which struggle to achieve short-term synaptic plasticity (STP). To address this issue and realize STP, we propose to leverage ferroelectric polarization to effectively draw ions across the interface so that the PV-SFO conductive filaments (CFs) can be ruptured in absence of an external field. As a proof of concept, we fabricate ferroelectric Pb(Zr0.2Ti0.8)O3 (PZT)/BM-SFO bilayer films with Au top electrodes and SrRuO3 bottom electrodes. The device exhibits the desired volatile resistive switching behavior, with its low resistance state decaying over time. Such volatility is attributed to the positive polarization charge near the PZT/SFO interface, which can attract the oxygen ions from SFO to PZT and hence lead to the rupture of CFs. Moreover, this volatile device successfully emulates STP-related synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, learning-experience behavior, associative learning, and reservoir computing. Our study showcases an effective method for achieving volatile resistive switching and STP, which may be applied to various systems beyond SFO-based memristors.

In this article, NaxCu1−xI thin-film transistors gated by chitosan for low-voltage operation have been created by doping CuI with different Na concentrations (x = 0, 0.05, 0.1). It is found that the devices exhibit optimal performance when x is 0.05: a large current on/off ratio of 1.62 × 105, a steep subthreshold slope of 17.72 mV/dec, a saturation field-effect mobility of 0.51 cm2/V s, and a threshold voltage of 1.10 V. The operating voltage of the devices is reduced to below 2 V due to the electric-double-layer (EDL) effect. At a frequency of 10 Hz, a maximum specific capacitance of 1.36 μF/cm2 can be obtained in the chitosan. The effects of bias stress and laser on Na0.05Cu0.95I thin-film transistors (TFTs) have been examined at the end of the article, and the results revealed that Na0.05Cu0.95I TFTs possess good stability. As the bias stress gets longer and the laser power increases, the transfer curves of TFTs shift positively. Also, artificial synaptic behaviors and functions have been simulated experimentally. It can be divided into single-pulse, double-pulse, and multiple-pulse. According to the experimental results, features such as short-term plasticity, long-term plasticity, paired-pulse facilitation, high-pass filtering, pulse logic, and spatial summation have been achieved. The electrostatic modulation of EDL due to proton transverse migration is vital for this simulation. The realization of low-voltage synaptic Na0.05Cu0.95I TFTs prepared by solution method with pulse logic and spatial summation functions is crucial for application of portable biosensors and neuromorphic systems.

Two-dimensional van der Waals heterostructures provide a versatile platform for multifunctional optoelectronic devices. WS2/PdSe2 heterostructures, characterized by n-type conductive behavior, exhibit persistent photoconductivity in high vacuum under negative gate voltages. This phenomenon has been leveraged to simulate synaptic functions of the human brain, including short- and long-term plasticity. Short-term plasticity was simulated, resulting in paired-pulse facilitation and post-tetanic potentiation indices of approximately 140% and 300%, respectively. By stimulating the WS2/PdSe2 heterostructure with consecutive 1 s light pulses, the learning, forgetting, and re-learning phases associated with knowledge acquisition in the human brain were replicated. Furthermore, the device was demonstrated as an optoelectronic memory, programmable with light pulses and erasable via electrical pulses on the gate electrode.

Emulating neurons/synapses in the brain is an important step to realizing highly efficient computers. This fact makes neuromorphic devices important emerging solutions to the limitations imposed by the current computing architecture. To mimic synaptic functions in the brain, it is critical to replicate ionic movements in the nervous system. It is therefore important to note that ions move easily in liquids. In this study, we demonstrate a liquid-based neuromorphic device that is capable of mimicking the movement of ions in the nervous system by controlling Na+ movement in an aqueous solution. The concentration of Na+ in the solution can control the ionic conductivity of the device. The device shows short-term and long-term plasticity such as excitatory postsynaptic current, paired-pulse facilitation, potentiation, and depression, which are key properties for memorization and computation in the brain. This device has the potential to overcome the limitations of current von Neumann architecture-based computing systems and substantially advance the technology of neuromorphic computing.

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.

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.

Neuromorphic computing has the great potential to enable faster and more energy‐efficient computing by overcoming the von Neumann bottleneck. However, most emerging nonvolatile memory (NVM)‐based artificial synapses suffer from insufficient precision, nonlinear synaptic weight update, high write voltage, and high switching latency. Moreover, the spatiotemporal dynamics, an important temporal component for cognitive computing in spiking neural networks (SNNs), are hard to generate with existing complementary metal–oxide–semiconductor (CMOS) devices or emerging NVM. Herein, a three‐terminal, LixWO3‐based electrochemical synapse (LiWES) is developed with low programming voltage (0.2 V), fast programming speed (500 ns), and high precision (1024 states) that is ideal for artificial neural networks applications. Time‐dependent synaptic functions such as paired‐pulse facilitation (PPF) and temporal filtering that are critical for SNNs are also demonstrated. In addition, by leveraging the spike‐encoded timing information extracted from the short‐term plasticity (STP) behavior in the LiWES, an SNNs model is built to benchmark the pattern classification performance of the LiWES, and the result indicates a large boost in classification performance (up to 128×), compared with those NO‐STP synapses.