Artificial Neuron and Synapse Realized in an Antiferromagnet/Ferromagnet Heterostructure Using Dynamics of Spin–Orbit Torque Switching

Efficient information processing in the human brain is achieved by dynamics of neurons and synapses, motivating effective implementation of artificial spiking neural networks. Here, the dynamics of spin–orbit torque switching in antiferromagnet/ferromagnet heterostructures is studied to show the capability of the material system to form artificial neurons and synapses for asynchronous spiking neural networks. The magnetization switching, driven by a single current pulse or trains of pulses, is examined as a function of the pulse width (1 s to 1 ns), amplitude, number, and pulse‐to‐pulse interval. Based on this dynamics and the unique ability of the system to exhibit binary or analog behavior depending on the device size, key functionalities of a synapse (spike‐timing‐dependent plasticity) and a neuron (leaky integrate‐and‐fire) are reproduced in the same material and on the basis of the same working principle. These results open a way toward spintronics‐based neuromorphic hardware that executes cognitive tasks with the efficiency of the human brain.     

Synaptic Barristor Based on Phase‐Engineered 2D Heterostructures

The development of energy‐efficient artificial synapses capable of manifoldly tuning synaptic activities can provide a significant breakthrough toward novel neuromorphic computing technology. Here, a new class of artificial synaptic architecture, a three‐terminal device consisting of a vertically integrated monolithic tungsten oxide memristor, and a variable‐barrier tungsten selenide/graphene Schottky diode, termed as a ‘synaptic barrister,’ are reported. The device can implement essential synaptic characteristics, such as short‐term plasticity, long‐term plasticity, and paired‐pulse facilitation. Owing to the electrostatically controlled barrier height in the ultrathin van der Waals heterostructure, the device exhibits gate‐controlled memristive switching characteristics with tunable programming voltages of 0.2?0.5 V. Notably, by electrostatic tuning with a gate terminal, it can additionally regulate the degree and tuning rate of the synaptic weight independent of the programming impulses from source and drain terminals. Such gate tunability cannot be accomplished by previously reported synaptic devices such as memristors and synaptic transistors only mimicking the two‐neuronal‐based synapse. These capabilities eventually enable the accelerated consolidation and conversion of synaptic plasticity, functionally analogous to the synapse with an additional neuromodulator in biological neural networks.     

Stimuli‐Enabled Artificial Synapses for Neuromorphic Perception: Progress and Perspectives

Brain‐inspired neuromorphic computing is intended to provide effective emulation of the functionality of the human brain via the integration of electronic components. Recent studies of synaptic plasticity, which represents one of the most significant neurochemical bases of learning and memory, have enhanced the general comprehension of how the brain functions and have thereby eased the development of artificial neuromorphic devices. An understanding of the synaptic plasticity induced by various types of stimuli is essential for neuromorphic system construction. The realization of multiple stimuli‐enabled synapses will be important for future neuromorphic computing applications. In this Review, state‐of‐the‐art synaptic devices with particular emphasis on their synaptic behaviors under excitation by a variety of external stimuli are summarized, including electric fields, light, magnetic fields, pressure, and temperature. The switching mechanisms of these synaptic devices are discussed in detail, including ion migration, electron/hole transfer, phase transition, redox‐based resistive switching, and other mechanisms. This Review aims to provide a comprehensive understanding of the operating mechanisms of artificial synapses and thus provides the principles required for design of multifunctional neuromorphic systems with parallel processing capabilities.     

Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing

Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.     

Observation of Resistive Switching Behavior in Crossbar Core–Shell Ni/NiO Nanowires Memristor

The crossbar structure of resistive random access memory (RRAM) is the most promising technology for the development of ultrahigh‐density devices for future nonvolatile memory. However, only a few studies have focused on the switching phenomenon of crossbar RRAM in detail. The main purpose of this study is to understand the formation and disruption of the conductive filament occurring at the crossbar center by real‐time transmission electron microscope observation. Core–shell Ni/NiO nanowires are utilized to form a cross‐structure, which restrict the position of the conductive filament to the crosscenter. A significant morphological change can be observed near the crossbar center, which results from the out‐diffusion and backfill of oxygen ions. Energy dispersive spectroscopy and electron energy loss spectroscopy demonstrate that the movement of the oxygen ions leads to the evolution of the conductive filament, followed by redox reactions. Moreover, the distinct reliability of the crossbar device is measured via ex situ experiments. In this work, the switching mechanism of the crossbar core–shell nanowire structure is beneficial to overcome the problem of nanoscale minimization. The experimental method shows high potential to fabricate high‐density RRAM devices, which can be applied to 3D stacked package technology and neuromorphic computing systems.     

Mediating Short‐Term Plasticity in an Artificial Memristive Synapse by the Orientation of Silica Mesopores

Memristive synapses based on resistive switching are promising electronic devices that emulate the synaptic plasticity in neural systems. Short‐term plasticity (STP), reflecting a temporal strengthening of the synaptic connection, allows artificial synapses to perform critical computational functions, such as fast response and information filtering. To mediate this fundamental property in memristive electronic devices, the regulation of the dynamic resistive change is necessary for an artificial synapse. Here, it is demonstrated that the orientation of mesopores in the dielectric silica layer can be used to modulate the STP of an artificial memristive synapse. The dielectric silica layer with vertical mesopores can facilitate the formation of a conductive pathway, which underlies a lower set voltage (≈1.0 V) compared to these with parallel mesopores (≈1.2 V) and dense amorphous silica (≈2.0 V). Also, the artificial memristive synapses with vertical mesopores exhibit the fastest current increase by successive voltage pulses. Finally, oriented silica mesopores are designed for varying the relaxation time of memory, and thus the successful mediation of STP is achieved. The implementation of mesoporous orientation provides a new perspective for engineering artificial synapses with multilevel learning and forgetting capability, which is essential for neuromorphic computing.     

Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device

We demonstrated analog memory, synaptic plasticity, and a spike-timing-dependent plasticity (STDP) function with a nanoscale titanium oxide bilayer resistive switching device with a simple fabrication process and good yield uniformity. We confirmed the multilevel conductance and analog memory characteristics as well as the uniformity and separated states for the accuracy of conductance change. Finally, STDP and a biological triple model were analyzed to demonstrate the potential of titanium oxide bilayer resistive switching device as synapses in neuromorphic devices. By developing a simple resistive switching device that can emulate a synaptic function, the unique characteristics of synapses in the brain, e.g. combined memory and computing in one synapse and adaptation to the outside environment, were successfully demonstrated in a solid state device.     

Photonic Potentiation and Electric Habituation in Ultrathin Memristive Synapses Based on Monolayer MoS2

Monolayer of 2D transition metal dichalcogenides, with a thickness of less than 1 nm, paves a feasible path to the development of ultrathin memristive synapses, to fulfill the requirements for constructing large‐scale high density 3D stacking neuromorphic chips. Herein, memristive devices based on monolayer n‐MoS₂ on p‐Si substrate with a large self‐rectification ratio, exhibiting photonic potentiation and electric habituation, are successfully fabricated. Versatile synaptic neuromorphic functions, such as potentiation/habituation, short‐term/long‐term plasticity, and paired‐pulse facilitation, are successfully mimicked based on the inherent persistent photoconductivity performance and the volatile resistive switching behavior. These findings demonstrate the potential applications of ultrathin transition metal dichalcogenides for memristive synapses. These memristive synapses with the combination of photonic and electric neuromorphic functions have prospective in the applications of synthetic retinas and optoelectronic interfaces for integrated photonic circuits based on mixed‐mode electro‐optical operation.     

Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO_2-X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.     

Near‐Infrared Annihilation of Conductive Filaments in Quasiplane MoSe2/Bi2Se3 Nanosheets for Mimicking Heterosynaptic Plasticity

It is desirable to imitate synaptic functionality to break through the memory wall in traditional von Neumann architecture. Modulating heterosynaptic plasticity between pre‐ and postneurons by another modulatory interneuron ensures the computing system to display more complicated functions. Optoelectronic devices facilitate the inspiration for high‐performance artificial heterosynaptic systems. Nevertheless, the utilization of near‐infrared (NIR) irradiation to act as a modulatory terminal for heterosynaptic plasticity emulation has not yet been realized. Here, an NIR resistive random access memory (RRAM) is reported, based on quasiplane MoSe₂/Bi₂Se₃ heterostructure in which the anomalous NIR threshold switching and NIR reset operation are realized. Furthermore, it is shown that such an NIR irradiation can be employed as a modulatory terminal to emulate heterosynaptic plasticity. The reconfigurable 2D image recognition is also demonstrated by an RRAM crossbar array. NIR annihilation effect in quasiplane MoSe₂/Bi₂Se₃ nanosheets may open a path toward optical‐modulated in‐memory computing and artificial retinal prostheses.     

Oxide-based RRAM materials for neuromorphic computing

In this review, a comprehensive survey of different oxide-based resistive random-access memories (RRAMs) for neuromorphic computing is provided. We begin with the history of RRAM development, physical mechanism of conduction, fundamental of neuromorphic computing, followed by a review of a variety of RRAM oxide materials (PCMO, HfO_x, TaO_x, TiO_x, NiO_x, etc.) with a focus on their application for neuromorphic computing. Our goal is to give a broad review of oxide-based RRAM materials that can be adapted to neuromorphic computing and to help further ongoing research in the field.     

2D MoS2 Neuromorphic Devices for Brain‐Like Computational Systems

Hardware implementation of artificial synapses/neurons with 2D solid‐state devices is of great significance for nanoscale brain‐like computational systems. Here, 2D MoS₂ synaptic/neuronal transistors are fabricated by using poly(vinyl alcohol) as the laterally coupled, proton‐conducting electrolytes. Fundamental synaptic functions, such as an excitatory postsynaptic current, paired‐pulse facilitation, and a dynamic filter for information transmission of biological synapse, are successfully emulated. Most importantly, with multiple input gates and one modulatory gate, spiking‐dependent logic operation/modulation, multiplicative neural coding, and neuronal gain modulation are also experimentally demonstrated. The results indicate that the intriguing 2D MoS₂ transistors are also very promising for the next‐generation of nanoscale neuromorphic device applications.     

Ion Gated Synaptic Transistors Based on 2D van der Waals Crystals with Tunable Diffusive Dynamics

Neuromorphic computing represents an innovative technology that can perform intelligent and energy‐efficient computation, whereas construction of neuromorphic systems requires biorealistic synaptic elements with rich dynamics that can be tuned based on a robust mechanism. Here, an ionic‐gating‐modulated synaptic transistor based on layered crystals of transitional metal dichalcogenides and phosphorus trichalcogenides is demonstrated, which produce a diversity of short‐term and long‐term plasticity including excitatory postsynaptic current, paired pulse facilitation, spiking‐rate‐dependent plasticity, dynamic filtering, etc., with remarkable linearity and ultralow energy consumption of ≈30 fJ per spike. Detailed transmission electron microscopy characterization and ab initio calculation reveal two‐stage ionic gating effects, namely, surface adsorption and internal intercalation in the channel medium, causing different poststimulation diffusive dynamics and thus accounting for the observed short‐term and long‐term plasticity, respectively. The synaptic activity can therefore be effectively manipulated by tailoring the ionic gating and consequent diffusion dynamics with varied thickness and structure of the van der Waals material as well as the number, duration, rate, and polarity of gate stimulations, making the present synaptic transistors intriguing candidates for low‐power neuromorphic systems.     

A Digital−Analog Bimodal Memristor Based on CsPbBr3 for Tactile Sensory Neuromorphic Computing

Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information-processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr₃-based memristor demonstrates a high ON/OFF ratio (>10³), long retention (>10⁴ s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5 × 5 spinosum-structured piezoresistive sensor array (sensitivity of 22.4 kPa⁻¹, durability of 1.5 × 10⁴ cycles, and fast response time of 2.43 ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr₃-based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5 × 5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital−analog bimodal memristor would demonstrate potential application in human–machine interaction, prosthetics, and artificial intelligence.     

Forming-free flexible memristor with multilevel storage for neuromorphic computing by full PVD technique

Flexible resistive random access memory(RRAM) has shown great potential in wearable electronics.With tunable multilevel resistance states,flexible memristors could be used to mimic the bio-synapses for constructing high-efficient wearable neuromorphic computing system.However,the flexible substrate has intrinsic disadvantages including low-tempe rature tolerance and poor complementary metal-oxidesemiconductor(CMOS) compatibility,which limit the development of flexible electronics.The physical vapor deposition(PVD) fabrication process could prepare RRAM without requirement of further treatment,which greatly simplified preparation steps and reduced the production costs.On the other hand,forming process,as a common pre-programing operation in RRAM,increases the energy consumption and limits the application scenarios of RRAM.Here,a NiO-based forming-free RRAM with low set voltage was fabricated via full PVD technique.The flexible device exhibited reliable re sistive switching characteristics under flat state even compre s sive and tensile states(R=10 mm).The tunable multilevel resistance states(5 levels) could be obtained by controlling the compliance current.Besides,synaptic plasticities also were verified in this device.The flexible NiO-based RRAM shows great potential in wearable forming-free multibit memo ry and neuromorphic computing electronics.     

Quasi‐Hodgkin–Huxley Neurons with Leaky Integrate‐and‐Fire Functions Physically Realized with Memristive Devices

Artificial neurons with functions such as leaky integrate‐and‐fire (LIF) and spike output are essential for brain‐inspired computation with high efficiency. However, previously implemented artificial neurons, e.g., Hodgkin–Huxley (HH) neurons, integrate‐and‐fire (IF) neurons, and LIF neurons, only achieve partial functionality of a biological neuron. In this work, quasi‐HH neurons with leaky integrate‐and‐fire functions are physically demonstrated with a volatile memristive device, W/WO₃/poly(3,4‐ethylenedioxythiophene): polystyrene sulfonate/Pt. The resistive switching behavior of the device can be attributed to the migration of protons, unlike the migration of oxygen ions normally involved in oxide‐based memristors. With multifunctions similar to their biological counterparts, quasi‐HH neurons are advantageous over the reported HH and LIF neurons, demonstrating their potential for neuromorphic computing applications.     

A MoS2/PTCDA Hybrid Heterojunction Synapse with Efficient Photoelectric Dual Modulation and Versatility

Just as biological synapses provide basic functions for the nervous system, artificial synaptic devices serve as the fundamental building blocks of neuromorphic networks; thus, developing novel artificial synapses is essential for neuromorphic computing. By exploiting the band alignment between 2D inorganic and organic semiconductors, the first multi‐functional synaptic transistor based on a molybdenum disulfide (MoS₂)/perylene‐3,4,9,10‐tetracarboxylic dianhydride (PTCDA) hybrid heterojunction, with remarkable short‐term plasticity (STP) and long‐term plasticity (LTP), is reported. Owing to the elaborate design of the energy band structure, both robust electrical and optical modulation are achieved through carriers transfer at the interface of the heterostructure, which is still a challenging task to this day. In electrical modulation, synaptic inhibition and excitation can be achieved simultaneously in the same device by gate voltage tuning. Notably, a minimum inhibition of 3% and maximum facilitation of 500% can be obtained by increasing the electrical number, and the response to different frequency signals indicates a dynamic filtering characteristic. It exhibits flexible tunability of both STP and LTP and synaptic weight changes of up to 60, far superior to previous work in optical modulation. The fully 2D MoS₂/PTCDA hybrid heterojunction artificial synapse opens up a whole new path for the urgent need for neuromorphic computation devices.     

Flexible Ionic‐Electronic Hybrid Oxide Synaptic TFTs with Programmable Dynamic Plasticity for Brain‐Inspired Neuromorphic Computing

Emulation of biological synapses is necessary for future brain‐inspired neuromorphic computational systems that could look beyond the standard von Neuman architecture. Here, artificial synapses based on ionic‐electronic hybrid oxide‐based transistors on rigid and flexible substrates are demonstrated. The flexible transistors reported here depict a high field‐effect mobility of ≈9 cm² V^?1 s^?1 with good mechanical performance. Comprehensive learning abilities/synaptic rules like paired‐pulse facilitation, excitatory and inhibitory postsynaptic currents, spike‐time‐dependent plasticity, consolidation, superlinear amplification, and dynamic logic are successfully established depicting concurrent processing and memory functionalities with spatiotemporal correlation. The results present a fully solution processable approach to fabricate artificial synapses for next‐generation transparent neural circuits.     

Synergies of Electrochemical Metallization and Valance Change in All‐Inorganic Perovskite Quantum Dots for Resistive Switching

The in‐depth understanding of ions' generation and movement inside all‐inorganic perovskite quantum dots (CsPbBr₃ QDs), which may lead to a paradigm to break through the conventional von Neumann bottleneck, is strictly limited. Here, it is shown that formation and annihilation of metal conductive filaments and Br^? ion vacancy filaments driven by an external electric field and light irradiation can lead to pronounced resistive‐switching effects. Verified by field‐emission scanning electron microscopy as well as energy‐dispersive X‐ray spectroscopy analysis, the resistive switching behavior of CsPbBr₃ QD‐based photonic resistive random‐access memory (RRAM) is initiated by the electrochemical metallization and valance change. By coupling CsPbBr₃ QD‐based RRAM with a p‐channel transistor, the novel application of an RRAM–gate field‐effect transistor presenting analogous functions of flash memory is further demonstrated. These results may accelerate the technological deployment of all‐inorganic perovskite QD‐based photonic resistive memory for successful logic application.     

Artificial Synapses Emulated by an Electrolyte‐Gated Tungsten‐Oxide Transistor

Considering that the human brain uses ≈10¹⁵ synapses to operate, the development of effective artificial synapses is essential to build brain‐inspired computing systems. In biological synapses, the voltage‐gated ion channels are very important for regulating the action‐potential firing. Here, an electrolyte‐gated transistor using WO₃ with a unique tunnel structure, which can emulate the ionic modulation process of biological synapses, is proposed. The transistor successfully realizes synaptic functions of both short‐term and long‐term plasticity. Short‐term plasticity is mimicked with the help of electrolyte ion dynamics under low electrical bias, whereas the long‐term plasticity is realized using proton insertion in WO₃ under high electrical bias. This is a new working approach to control the transition from short‐term memory to long‐term memory using different gate voltage amplitude for artificial synapses. Other essential synaptic behaviors, such as paired pulse facilitation, the depression and potentiation of synaptic weight, as well as spike‐timing‐dependent plasticity are also implemented in this artificial synapse. These results provide a new recipe for designing synaptic electrolyte‐gated transistors through the electrostatic and electrochemical effects.