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.     

Influence of Geometry on the Memristive Behavior of the Domain Wall Spintronic Memristors and Its Applications for Measurement

A memristor is characterized by its electrical memory resistance (memristance), which is a function of the historic profile of the applied current (voltage). This unique ability allows reducing charge- and flux-based measurements to straightforward resistance measurements. The memristive measurement seeks a memristor with a constant modulation of the memristance (memductance) with respect to the charge (flux) for charge (flux)-based measurements. In this work the geometry dependent memristive behavior of a spintronic device is studied to demonstrate the possibility of both charge- and flux-based sensing, using spintronic memristors with different device geometries. The dynamic properties of a propagating magnetic domain wall in different geometrical structures make the spintronic memristor suitable for the charge-based capacitance and flux-based inductance measurements.     

Controlled stochastization of oscillations in a chaotic delayed-feedback system

A mathematical model of the ring autooscillatory system, which possesses chaotic dynamics and comprises a nonlinear amplifier with a differentiating element in the feedback chain, a nonlinear oscillatory circuit, and a delay line, is considered. Numerical analysis has been performed for irregularly varying initial conditions. These conditions were set by the solutions of equations describing chaos that simulated intrinsic noises of a real autooscillatory system. It is shown that the irregularly varying conditions of excitation can lead to stochastization of the chaotic oscillations, in which case the oscillatory process becomes irreproducible.     

混沌神经元耦合置乱神经元的图像加密算法研究

目的为了使当前加密系统具有更强的密钥敏感性以及更大的密钥空间,以提高抗各种攻击性能。方法提出一种新型的基于置乱神经元耦合混沌神经元的图像加密算法。加密系统的置乱和扩散由2个不同的3层神经构成,分别是置乱神经元层和混沌神经元层,混沌密钥生成模块则通过相应的权值和偏置来对这2层结构进行控制。在混沌神经元层扩散过程中,3个混沌系统用来生成权值矩阵和偏置矩阵,通过非线性标准化、按位异或操作来进行非线性组合,并通过Tent映射来进行激活,以获得扩散信息。在置乱神经元层置乱过程中,利用混沌密钥生成模块获取置乱矩阵,对扩散信息进行线性置乱处理,再通过二维Cat混沌映射对信息进行非线性置乱处理,并与当前加密算法进行对比。结果与当前加密算法相比,文中算法安全性更高,平均熵值为7.9991,且该加密算法的密钥空间大,为2160×1060,密钥敏感性强,错误与正确密钥之间的密文差异率为99.765%。结论设计的加密算法高度安全,可有效抗击各种攻击。     

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.     

Atomically Thin Femtojoule Memristive Device

The morphology and dimension of the conductive filament formed in a memristive device are strongly influenced by the thickness of its switching medium layer. Aggressive scaling of this active layer thickness is critical toward reducing the operating current, voltage, and energy consumption in filamentary‐type memristors. Previously, the thickness of this filament layer has been limited to above a few nanometers due to processing constraints, making it challenging to further suppress the on‐state current and the switching voltage. Here, the formation of conductive filaments in a material medium with sub‐nanometer thickness formed through the oxidation of atomically thin two‐dimensional boron nitride is studied. The resulting memristive device exhibits sub‐nanometer filamentary switching with sub‐pA operation current and femtojoule per bit energy consumption. Furthermore, by confining the filament to the atomic scale, current switching characteristics are observed that are distinct from that in thicker medium due to the profoundly different atomic kinetics. The filament morphology in such an aggressively scaled memristive device is also theoretically explored. These ultralow energy devices are promising for realizing femtojoule and sub‐femtojoule electronic computation, which can be attractive for applications in a wide range of electronics systems that desire ultralow power operation.     

Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics

The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain-inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low-dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding physical properties and electrical tunability, low-power-switching capability, and hetero-integration compatibility. Hence, a large number of experimental demonstrations on 2D material-based memristors have been reported showing their unique memristive characteristics and novel synaptic functionalities, distinct from traditional bulk-material-based systems. Herein, an overview of the latest advances in the structures, mechanisms, and memristive characteristics of 2D material-based memristors is presented. Additionally, novel strategies to modulate and enhance the synaptic functionalities of 2D-memristor-based artificial synapses are summarized. Finally, as a foreseeing perspective, the potentials and challenges of these emerging materials for future neuromorphic electronics are also discussed.     

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.     

一步掩膜法制备ZnO纳米线忆阻器

本文基于单根ZnO纳米线(NW),采用一步掩膜的方法制备了Au/ZnO NW/Au忆阻器。器件表现出无极性忆阻行为,开关比可达10~5以上。低阻态具有半导体导电特性,推测忆阻行为可能来源于ZnO NW表面氧空位形成的不连续导电丝的通断。一步掩膜法工艺简单,制备过程对器件污染少,因此是制备纳米线器件的有效方法。     

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.     

Electrically Controlled Nano and Micro Actuation in Memristive Switching Devices with On‐Chip Gas Encapsulation

Nanoactuators are a key component for developing nanomachinery. Here, an electrically driven device yielding actuation stresses exceeding 1 MPa withintegrated optical readout is demonstrated. 10 nm thick Al₂O₃ electrolyte films are sandwiched between graphene and Au electrodes. These allow reversible room‐temperature solid‐state redox reactions, producing Al metal and O₂ gas in a memristive‐type switching device. The resulting high‐pressure oxygen micro‐fuel reservoirs are encapsulated under the graphene, swelling to heights of up to 1 µm, which can be dynamically tracked by plasmonic rulers. Unlike standard memristors where the memristive redox reaction occurs in single or few conductive filaments, the mechanical deformation forces the creation of new filaments over the whole area of the inflated film. The resulting on–off resistance ratios reach 10⁸ in some cycles. The synchronization of nanoactuation and memristive switching in these devices is compatible with large‐scale fabrication and has potential for precise and electrically monitored actuation technology.     

一个具有多翼吸引子的四维多稳态超混沌系统

构造了一个只有一个平衡点的四维超混沌系统,此系统表现出丰富的多稳态特性,亦具有多翼吸引子。数值分析了系统的动力学特性,仿真了系统的模拟电路和数字电路,探讨了系统的动态复杂度,测试了系统超混沌序列的随机性。分析结果表明,在多组参数值下,系统均存在不同类型的吸引子共存,譬如:两个周期吸引共存,周期与拟周期吸引子共存,双翼混沌与超混沌吸引子共存,两个双翼混沌吸引子共存,双翼与四翼混沌吸引子共存,两个双翼超混沌吸引子共存,两个双翼拟周期吸引子共存,两个双翼超混沌、四翼混沌、四翼超混沌等四个吸引子共存。系统的数字电路和模拟电路的仿真结果均与数值分析结果一致,表明了系统的可实现性。另外,在混沌和超混沌状态下系统复杂度高,且超混沌序列通过了SP800-22 Revla的15项随机测试。     

Dynamical Analysis of Rub-impact Rotor System with Asymmetric Oil Film Force

Considering the effect of non-symmetry film force,nonlinear stiffness and nonlinear friction force,a dynamical model of rub-impact rotor system is established,then the nonlinear dynamical behavior is studied by numerical analysis method.The effect of rotation speed,nonlinear stiffness ratio and speed effect factor on brifurcation and chaotic behavior for rub-impact rotor system is comprehensively analyzed.The analysis results show that the effect of non-symmetry film force,nonlinear stiffness and nonlinear friction force on the dynamical behavior of the rotor system has close relation with rotation speed.The chaotic behavior exists in a wider parameter region,and the chaotic evolution rule is more complicated.The research provides a reliable theory basis and reference for diagnosing some faults of the rotor system.     

Studies on switching mechanisms in Pd-nanodot embedded Nb2O5 memristors using scanning tunneling microscopy

Current imaging tunneling spectrum obtained from scanning tunneling microscopy has been used to probe the formation and/or rupture of conductive filaments responsible for bipolar switching in Pd nano-dots embedded Nb₂O₅ memristors. Filamentary conduction mechanism has been confirmed by scanning tunneling microscopy study using a Pt-Ir tip that enabled performing electroforming and reset operations at the nanoscale. The back and forth transition between the fully oxidized and metallic sub-oxide states of niobium under applied bias, as observed from X-ray photoelectron spectroscopy, is believed to be the source of bipolar switching in Nb₂O₅ memristors. The incorporation of Pd nanodots in Nb₂O₅ matrix plays a critical role by acting as an oxygen ion reservoir and/or by polarizing a large volume of oxygen vacancies. The formation and/or rupture of the conducting filaments through trapping-detrapping phenomena are found to boost the memristive switching performance.     

Multi-Wavelength-Recognizable Memristive Devices via Surface Plasmon Resonance Effect for Color Visual System

Photoelectric memristor has attracted many attentions thanks to their promising potential in optical communication chips and artificial vision systems. However, the implementation of an artificial visual system based on memristive devices remains a considerable challenge because most photoelectric memristors cannot recognize color. Herein, multi-wavelength recognizable memristive devices based on silver(Ag) nanoparticles (NPs) and porous silicon oxide (SiO_x) nanocomposites are presented. Rely on the effects of localized surface plasmon resonance (LSPR) and optical excitation of Ag NPs in SiO_x, the set voltage of the device can be gradually reduced. Moreover, the current overshoot problem is alleviated to suppress conducting filament overgrowth after visible light irradiation with different wavelengths, resulting in diverse low resistance states (LRS). Taking advantage of the characteristics of controlled switching voltage and LRS resistance distribution, color image recognition is finally realized in the present work. X-ray photoelectron spectroscopy (XPS) and conductive atomic force microscopy (C-AFM) show that the light irradiation plays an important role on resistive switching (RS) process: the photo-assisted Ag ionization leads to a significant reduction of set voltage and overshoot current. This work provides an effective method toward the development of multi-wavelength-recognizable memristive devices for future artificial color vision system.     

Coupled Ionic and Electronic Transport Model of Thin‐Film Semiconductor Memristive Behavior

The memristor, the fourth passive circuit element, was predicted theoretically nearly 40 years ago, but we just recently demonstrated both an intentional material system and an analytical model that exhibited the properties of such a device. Here we provide a more physical model based on numerical solutions of coupled drift‐diffusion equations for electrons and ions with appropriate boundary conditions. We simulate the dynamics of a two‐terminal memristive device based on a semiconductor thin film with mobile dopants that are partially compensated by a small amount of immobile acceptors. We examine the mobile ion distributions, zero‐bias potentials, and current–voltage characteristics of the model for both steady‐state bias conditions and for dynamical switching to obtain physical insight into the transport processes responsible for memristive behavior in semiconductor films.     

Atomic origin of the traps in memristive interface

In recent years,trap-related interfacial transport phenomena have received great attention owing to their potential applications in resistive switching devices and photo detectors.Not long ago,one new type of memristive interface that is composed of F-doped SnO2 and Bi2S3 nano-network layers has demonstrated a bivariate-continuous-tunable resistance with a swift response comparable to the one in neuron synapses and with a brain-like memorizing capability.However,the resistive mechanism is still not clearly understood because of lack of evidence,and the limited improvement in the development of the interfacial device.By combining I-V characterization,electron energy-loss spectroscopy,and firstprinciple calculation,we studied in detail the macro/micro features of the memristive interface using experimental and theoretical methods,and confirmed that its atomic origin is attributed to the traps induced by O-doping.This implies that impurity-doping might be an effective strategy for improving switching features and building new interfacial memristors.     

混沌振动的自适应预测方法研究

在混沌时间序列相空间重构的基础上,提出了一种沿轨道自适应预测方法,推导了模型参数的自适应算法,并研究了不同邻域点数、重构嵌入维数对预测结果的影响,仿真研究结果表明在一定的参数条件下,该方法具有良好的预测性能。将该方法用于某型非线性隔振系统振动实验信号的预测,取得了良好的预测效果。     

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。     

An Effective Sneak-Path Solution Based on a Transient-Relaxation Device

An efficient strategy for addressing individual devices is required to unveil the full potential of memristors for high-density memory and computing applications. Existing strategies using two-terminal selectors that are preferable for compact integration have trade-offs in reduced generality or functional window. A strategy that applies to broad memristors and maintains their full-range functional window is proposed. This strategy uses a type of unipolar switch featuring a transient relaxation or retention as the selector. The unidirectional current flow in the switch suppresses the sneak-path current, whereas the transient-relaxation window is exploited for bidirectional programming. A unipolar volatile memristor with ultralow switching voltage (e.g., <100 mV), constructed from a protein nanowire dielectric harvested from Geobacter sulfurreducens, is specifically employed as the example switch to highlight the advantages and scalability in the strategy for array integration.