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.     

A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications

Crossbar arrays based on two-terminal resistive switches have been proposed as a leading candidate for future memory and logic applications. Here we demonstrate a high-density, fully operational hybrid crossbar/CMOS system composed of a transistor- and diode-less memristor crossbar array vertically integrated on top of a CMOS chip by taking advantage of the intrinsic nonlinear characteristics of the memristor element. The hybrid crossbar/CMOS system can reliably store complex binary and multilevel 1600 pixel bitmap images using a new programming scheme.     

Low‐Power,Electrochemically Tunable Graphene Synapses for Neuromorphic Computing

Brain‐inspired neuromorphic computing has the potential to revolutionize the current computing paradigm with its massive parallelism and potentially low power consumption. However, the existing approaches of using digital complementary metal–oxide–semiconductor devices (with “0” and “1” states) to emulate gradual/analog behaviors in the neural network are energy intensive and unsustainable; furthermore, emerging memristor devices still face challenges such as nonlinearities and large write noise. Here, an electrochemical graphene synapse, where the electrical conductance of graphene is reversibly modulated by the concentration of Li ions between the layers of graphene is presented. This fundamentally different mechanism allows to achieve a good energy efficiency (<500 fJ per switching event), analog tunability (>250 nonvolatile states), good endurance, and retention performances, and a linear and symmetric resistance response. Essential neuronal functions such as excitatory and inhibitory synapses, long‐term potentiation and depression, and spike timing dependent plasticity with good repeatability are demonstrated. The scaling study suggests that this simple, two‐dimensional synapse is scalable in terms of switching energy and speed.     

Demonstration of synaptic and resistive switching characteristics in W/TiO2/HfO2/TaN memristor crossbar array for bioinspired neuromorphic computing

In this study,resistive random-access memory (RRAM)-based crossbar arrays with a memristor W/TiO2/HfO2/TaN structure were fabricated through atomic layer deposi...     

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.     

An Atomically Thin Optoelectronic Machine Vision Processor

2D semiconductors, especially transition metal dichalcogenide (TMD) monolayers, are extensively studied for electronic and optoelectronic applications. Beyond intensive studies on single transistors and photodetectors, the recent advent of large-area synthesis of these atomically thin layers has paved the way for 2D integrated circuits, such as digital logic circuits and image sensors, achieving an integration level of ≈100 devices thus far. Here, a decisive advance in 2D integrated circuits is reported, where the device integration scale is increased by tenfold and the functional complexity of 2D electronics is propelled to an unprecedented level. Concretely, an analog optoelectronic processor inspired by biological vision is developed, where 32 × 32 = 1024 MoS₂ photosensitive field-effect transistors manifesting persistent photoconductivity (PPC) effects are arranged in a crossbar array. This optoelectronic processor with PPC memory mimics two core functions of human vision: it captures and stores an optical image into electrical data, like the eye and optic nerve chain, and then recognizes this electrical form of the captured image, like the brain, by executing analog in-memory neural net computing. In the highlight demonstration, the MoS₂ FET crossbar array optically images 1000 handwritten digits and electrically recognizes these imaged data with 94% accuracy.     

Physically Transient Threshold Switching Device Based on Magnesium Oxide for Security Application

Transient memristors are prospective candidates for both secure memory systems and biointegrated electronics, which are capable to physically disappear at a programmed time with a triggered operation. However, the sneak current issue has been a considerable obstacle to achieve high‐density transient crossbar array of memristors. To solve this problem, it is necessary to develop a transient switch device to turn the memory device on and off controllably. Here, a dissolvable and flexible threshold switching (TS) device with a vertically crossed structure is introduced, which exhibits a high selectivity of 10⁷, steep turn‐on slope of <8 mV dec⁻¹, and fast ON/OFF switch speed within 50/25 ns. Triggered failure could be achieved after soaking the device in deionized water for 8 min at room temperature. Furthermore, a water‐assisted transfer printing method is used to fabricate flexible and transient TS device arrays for bioresorbable systems, in which none of any significant degradation is observed under a bending radius of 2 mm. Integrating the selector with a transient memristor is capable of 10⁷ Gb memory implementation, indicating that the transient TS device could provide great opportunities to achieve highly integrated transient memory arrays.     

Bio-plausible memristive neural components towards hardware implementation of brain-like intelligence

A memristor can comprehensively emulate the neural components rather than imitating a single characteristic superficially due to its analog and hysteretic resistive switching. Bio-plausible mimicry aims to emulate biological working mechanisms to implement the complicated functional characteristics of a neural network for artificial intelligence (AI). Bio-plausible neuromorphic device using memristor is a direct and efficient approach for the emulation of biological systems, contributing to the realization of brain-like intelligence beyond limited AI applications. In this article, we review recent progress in bio-plausible mimicry of neural components using memristive devices. Memristor-based artificial neurons, synapses, and nerve systems are discussed focusing on the analogy in the operation mechanisms. In addition, we explore the neurological and interdisciplinary approaches in neuronal mapping and brain-computer interfaces to place an emphasis on the relationship between memristive neuromorphic system and biological neural network.     

In-Memory Computing with Memristor Content Addressable Memories for Pattern Matching

The dramatic rise of data-intensive workloads has revived application-specific computational hardware for continuing speed and power improvements, frequently achieved by limiting data movement and implementing “in-memory computation”. However, conventional complementary metal oxide semiconductor (CMOS) circuit designs can still suffer low power efficiency, motivating designs leveraging nonvolatile resistive random access memory (ReRAM), and with many studies focusing on crossbar circuit architectures. Another circuit primitive—content addressable memory (CAM)—shows great promise for mapping a diverse range of computational models for in-memory computation, with recent ReRAM–CAM designs proposed but few experimentally demonstrated. Here, programming and control of memristors across an 86 × 12 memristor ternary CAM (TCAM) array integrated with CMOS are demonstrated, and parameter tradeoffs for optimizing speed and search margin are evaluated. In addition to smaller area, this memristor TCAM results in significantly lower power due to very low programmable conductance states, motivating CAM use in a wider range of computational applications than conventional TCAMs are confined to today. Finally, the first experimental demonstration of two computational models in memristor TCAM arrays is reported: regular expression matching in a finite state machine for network security intrusion detection and definable inexact pattern matching in a Levenshtein automata for genomic sequencing.     

Truly Concomitant and Independently Expressed Short‐ and Long‐Term Plasticity in a Bi2O2Se‐Based Three‐Terminal Memristor

Concomitance of diverse synaptic plasticity across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive neuromorphic systems, devices that are capable of emulating short‐term (STP) and long‐term plasticity (LTP) concomitantly are essential. In existing memristors, however, STP and LTP can only be induced selectively because of the inability to be decoupled using different loci and mechanisms. In this work, the first demonstration of truly concomitant STP and LTP is reported in a three‐terminal memristor that uses independent physical phenomena to represent each form of plasticity. The emerging layered material Bi₂O₂Se is used for memristors for the first time, opening up the prospects for ultrathin, high‐speed, and low‐power neuromorphic devices. The concerted action of STP and LTP allows full‐range modulation of the transient synaptic efficacy, from depression to facilitation, by stimulus frequency or intensity, providing a versatile device platform for neuromorphic function implementation. A heuristic recurrent neural circuitry model is developed to simulate the intricate “sleep–wake cycle autoregulation” process, in which the concomitance of STP and LTP is posited as a key factor in enabling this neural homeostasis. This work sheds new light on the development of generic memristor platforms for highly dynamic neuromorphic computing.     

Two‐Dimensional Self‐Organization of an Ordered Au Silicide Nanowire Network on a Si(110)‐16 × 2 Surface

A well‐ordered two‐dimensional (2D) network consisting of two crossed Au silicide nanowire (NW) arrays is self‐organized on a Si(110)‐16 × 2 surface by the direct‐current heating of ≈1.5 monolayers of Au on the surface at 1100 K. Such a highly regular crossbar nanomesh exhibits both a perfect long‐range spatial order and a high integration density over a mesoscopic area, and these two self‐ordering crossed arrays of parallel‐aligned NWs have distinctly different sizes and conductivities. NWs are fabricated with widths and pitches as small as ≈2 and ≈5 nm, respectively. The difference in the conductivities of two crossed‐NW arrays opens up the possibility for their utilization in nanodevices of crossbar architecture. Scanning tunneling microscopy/spectroscopy studies show that the 2D self‐organization of this perfect Au silicide nanomesh can be achieved through two different directional electromigrations of Au silicide NWs along different orientations of two nonorthogonal 16 × 2 domains, which are driven by the electrical field of direct‐current heating. Prospects for this Au silicide nanomesh are also discussed.     

高均一性二维碲化钼忆阻器阵列及其神经形态计算应用

二维过渡金属硫化合物是构建纳米电子器件的理想材料, 基于该材料体系开发用于信息存储和神经形态计算的忆阻器, 受到了学术界的广泛关注。受制于低成品率和低均一性问题, 二维过渡金属硫化合物忆阻器阵列鲜见报道。本研究采用化学气相沉积得到厘米级二维碲化钼薄膜, 并通过湿法转移和剥离工艺制备得到碲化钼忆阻器件。该碲化钼器件表现出优异的保持性(保持时间>500 s)、快速的阻变(SET时间~60 ns, RESET时间~280 ns)和较好的循环寿命(阻变2000圈后仍可正常工作)。该器件具有高成品率(96%)、低阻变循环间差异性(SET过程为6.6%, RESET过程为5.2%)和低器件间差异性(SET过程为19.9%, RESET过程为15.6%)。本工作成功制备出基于MoTe₂的3×3忆阻器阵列。在此基础上, 将研制的MoTe₂器件用于手写体识别, 实现了91.3%的识别率。最后, 通过对MoTe₂器件高低阻态的电子输运机制进行拟合分析, 揭示了该器件阻变源于类金属导电细丝的通断过程。本项工作表明大尺寸二维过渡金属硫化合物在未来神经形态计算中具有巨大的应用潜力。     

矢量调制法的测量精度研究

以使用光纤陀螺进行地速测量为例，介绍矢量调制法原理，研究矢量调制法测量机理。理论分析和实验结果表明，使用适当的数据算法和增益可以提高测量系统的绝对精度，并且测量系统的相对精度高于绝对精度。矢量调制法经过调制一解调，使用频域方法去除了偏置、漂移等测量误差，可提高测量精度。矢量调制法的理论分析与实验分析结果一致，正确地进行了高精度测量。     

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.     

Vacancy‐Induced Synaptic Behavior in 2D WS2 Nanosheet–Based Memristor for Low‐Power Neuromorphic Computing

Memristors with nonvolatile memory characteristics have been expected to open a new era for neuromorphic computing and digital logic. However, existing memristor devices based on oxygen vacancy or metal‐ion conductive filament mechanisms generally have large operating currents, which are difficult to meet low‐power consumption requirements. Therefore, it is very necessary to develop new materials to realize memristor devices that are different from the mechanisms of oxygen vacancy or metal‐ion conductive filaments to realize low‐power operation. Herein, high‐performance and low‐power consumption memristors based on 2D WS₂ with 2H phase are demonstrated, which show fast ON (OFF) switching times of 13 ns (14 ns), low program current of 1 µA in the ON state, and SET (RESET) energy reaching the level of femtojoules. Moreover, the memristor can mimic basic biological synaptic functions. Importantly, it is proposed that the generation of sulfur and tungsten vacancies and electron hopping between vacancies are dominantly responsible for the resistance switching performance. Density functional theory calculations show that the defect states formed by sulfur and tungsten vacancies are at deep levels, which prevent charge leakage and facilitate the realization of low‐power consumption for neuromorphic computing application.     

Zero-static-power nonvolatile logic-in-memory circuits for flexible electronics

Flexible logic circuits and memory with ultra-low static power consumption are in great demand for battery-powered flexible electronic systems.Here,we show that a flexible nonvolatile logic-in-memory circuit enabling normally-off computing can be implemented using a poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based memristor array.Although memristive logic-in-memory circuits have been previously reported,the requirements of additional components and the large variation of memristors have limited demonstrations to simple gates within a few operation cycles on rigid substrates only.Using memristor-aided logic (MAGIC) architecture requiring only memristors and pV3D3-memristor with good uniformity on a flexible substrate,for the first time,we experimentally demonstrated our implementation of MAGIC-NOT and-NOR gates during multiple cycles and even under bent conditions.Other functions,such as OR,AND,NAND,and a half adder,are also realized by combinations of NOT and NOR gates within a crossbar array.This research advances the development of novel computing architecture with zero static power consumption for batterypowered flexible electronic systems.     

Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.     

Internal ion transport in ionic 2D CuInP2S6 enabling multi-state neuromorphic computing with low operation current

Memristor-based neuromorphic computing is promising for artificial intelligence. However, most of the reported memristors have limited linear computing states and consume large operation energy which hinder their applications. Herein, we report a memristor based on ionic two-dimensional CuInP₂S₆ (2D CIPS), in which up to 1350 linear conductance states are achieved by controlling the migration of internal Cu ions in CIPS. In addition, the device shows a low operation current of ∼100 pA. Cu ions are proven to move along the electric field by in-situ scanning electron microscopy and energy dispersive spectroscopy measurements. Furthermore, complex signal transport among multiple neurons in the brain is imitated by 2D CIPS-based memristor arrays. Our results offer a new platform to fabricate high-performance memristors based on ion transport in 2D materials for neuromorphic computing.     

基于微分进化神经网络的模拟电路故障诊断

BP神经网络已在模拟电路故障诊断领域得到广泛应用,但BP神经网络存在训练速度慢且容易陷入局部最优的问题.由此,本文提出了一种基于混合变异策略的微分进化改进算法,描述了利用微分进化改进算法进行神经网络权值训练的过程和方法,并将微分进化神经网络用于模拟电路故障诊断,文中还对微分进化神经网络与BP神经网络进行了比较.实验结果表明,微分进化神经网络的训练时间和训练精度均优于BP神经网络,其在模拟电路故障诊断中的准确度比BP神经网络提高了7%.     

A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O(5-x)/TaO(2-x) bilayer structures

Numerous candidates attempting to replace Si-based flash memory have failed for a variety of reasons over the years. Oxide-based resistance memory and the related memristor have succeeded in surpassing the specifications for a number of device requirements. However, a material or device structure that satisfies high-density, switching-speed, endurance, retention and most importantly power-consumption criteria has yet to be announced. In this work we demonstrate a TaO(x)-based asymmetric passive switching device with which we were able to localize resistance switching and satisfy all aforementioned requirements. In particular, the reduction of switching current drastically reduces power consumption and results in extreme cycling endurances of over 10(12). Along with the 10 ns switching times, this allows for possible applications to the working-memory space as well. Furthermore, by combining two such devices each with an intrinsic Schottky barrier we eliminate any need for a discrete transistor or diode in solving issues of stray leakage current paths in high-density crossbar arrays.