ZnO全光控忆阻器及其类突触行为

本研究基于ZnO制备了一种全光控忆阻器,短波光照射可增大器件电导,长波光则可降低电导,并且电导态可以长时间保持.因此,通过改变施加光信号的波长,可实现忆阻器电导的可逆调控.基于以上特性,该器件可以模拟突触基本功能,包括长程增强与长程抑制、光功率密度依赖可塑性、频率依赖可塑性以及学习-遗忘-再学习的经验学习行为.与电相比,光具有高带宽、低串扰、速度快等优势,并且不改变器件微结构,因此全光控忆阻器有望应用于类脑智能系统的构建.     

基于ZnO忆阻器的神经突触仿生电子器件

本文采用ZnO忆阻器模拟了生物神经突触的记忆和学习功能。ZnO突触器件表现出典型的随时间指数衰减的突触后兴奋电流(EPSC),以及EPSC的双脉冲增强行为。在此基础上,实现了学习-遗忘-再学习的经验式学习行为,以及四种不同种类的电脉冲时刻依赖可塑性学习规则。ZnO突触器件实现了超低能耗操作,单次突触行为能耗最低为1.6pJ,表明其可以用来构筑未来的人工神经网络硬件系统,最终开发出与人脑结构类似的认知型计算机以及类人机器人。     

面向类脑计算的氧化物忆阻器

类脑神经形态计算通过电子或光子器件集成来模拟人脑结构和功能。人工突触是类脑系统中数量最多的计算单元。忆阻器可模拟突触功能,并具有优异的尺寸缩放性和低能耗,是实现人工突触的理想元器件。利用欧姆定律和基尔霍夫定律,忆阻器交叉阵列可执行并行的原位乘累加运算,从而大幅提升类脑系统处理模拟信号的速度。氧化物制备容易,和CMOS工艺兼容性强,是使用最广泛的忆阻器材料。本文梳理了氧化物忆阻器的研究进展,分别讨论了电控、光电混合调控和全光控忆阻器,主要聚焦阻变机理、器件结构和性能。电控忆阻器工作一般会产生微结构变化和焦耳热,将严重影响器件稳定性,改进器件结构和材料成分可有效改善器件性能。利用光信号调控忆阻器电导,不仅能降低能耗,而且可避免产生微结构变化和焦耳热,从而有望解决稳定性难题。此外,光控忆阻器能直接感受光刺激,单器件即可实现感/存/算功能,可用于研发新型视觉传感器。因此,全光控忆阻器的实现为忆阻器的研究和应用打开了一扇新窗口。     

CuS/ZnS/ITO透明忆阻器的制备及其突触性能

忆阻器突触可用于构建神经形态系统,进行类脑计算,而透明突触器件则有利于光电协同调控.本研究首次采用CuS薄膜作为电极,构筑了CuS/ZnS/ITO透明忆阻器,器件表现出稳定的忆阻性能与良好的均一性,在可见光范围内透过率高达82％.通过与Cu制电极的器件比较,采用CuS制电极可以抑制Cu离子向ZnS介质层中大量迁移,有利于提高器件稳定性.在此基础上,通过施加不同形式的电脉冲信号,可以调节忆阻器件的阻态,实现突触可塑性模拟.CuS/ZnS/ITO器件在未来透明神经形态器件领域具有重要的应用价值.     

氧化物双介质层忆阻器的设计及应用

忆阻器可以在单一器件上实现存储和计算功能,成为打破冯·诺依曼瓶颈的核心电子元器件之一。它凭借独特的易失性/非易失性电阻特性,可以很好地模拟大脑活动中的突触/神经元的功能。此外,基于金属氧化物的忆阻器与传统的互补金属氧化物半导体(CMOS)工艺兼容,受到了广泛关注。近年来,研究提出了多种基于单介质层结构的金属氧化物忆阻器,但仍然存在高低阻态不稳定、开关电压波动大和循环耐久性差等问题。在此基础上,研究人员通过在金属氧化物忆阻器中引入双介质层成功优化了忆阻器的性能。本文首先详细介绍了氧化物双介质层忆阻器的优势,阐述了氧化物双介质层忆阻器的阻变机理和设计思路,并进一步介绍了氧化物双介质层忆阻器在神经形态计算中的应用。本文将为设计更高性能的氧化物双介质层忆阻器起到一定的启示作用。     

美制成与大脑突触类似的忆阻器电路

日前,美国密歇根大学的一个研究小组称制成了一种模拟大脑突触的忆阻器电路,证实了此前关于忆阻器能用于电脑神经网络制作的设想。相关论文发表在最新一期的《纳米快报》杂志上。     

基于氧化物基电解质栅控晶体管突触的关联学习

电解质栅控晶体管(Electrolyte-gated transistors, EGTs)的沟道电导连续可调特性使其在构建神经形态计算系统中具有巨大应用潜力。本工作以非晶态Nb₂O₅作为沟道材料, Li_xSiO₂作为栅电解质材料,制备了一种具备低沟道电导(～120n S)的EGT器件。该器件利用Li⁺嵌入/脱出Nb₂O₅晶格导致的沟道电导连续可逆变化,模拟了神经突触的短程可塑性(Short-termplasticity,STP)、长程可塑性(Long-termplasticity,LTP)以及STP向LTP的转变等功能。基于这种EGT突触特性,本工作设计了关联学习电路,实现了突触权重的负反馈调节,并模拟了“巴普洛夫的狗”经典条件反射行为。这些结果展现出EGT作为神经突触器件的巨大潜力,为实现神经形态计算硬件提供了器件参考。     

基于电化学金属化机制的电子突触电导可控性研究进展

随着数据信息的爆炸性增长和微电子加工工艺逼近物理极限,互补金属氧化物半导体(CMOS)器件难以应用于大规模神经形态器件的构建。采用非CMOS器件实现突触可塑性模拟被认为是后摩尔时代构造人工神经网络的关键。在众多的非CMOS器件中,忆阻器具有电导可调、结构简单等优点,被认为是再现神经突触功能、实现计算存储一体化的基础元件。在众多类型的忆阻器中,基于电化学金属化机制(ECM)的忆阻器具有机理明确、可超高密度集成、对材料属性不敏感等优点,特别适合应用于电子突触的构建。但ECM电子突触存在着电导可控性不足的问题,制约着高性能神经形态器件的实现。国内外研究人员针对ECM电子突触的电导可控性展开了大量研究。本综述从器件结构和材料角度梳理了ECM电子突触电导可控性的优化方法。     

氧化铁忆阻器中缺陷态诱导的模拟型阻变及突触双脉冲易化特性

模拟型阻变突触特性能够为神经形态计算提供高的计算精度并避免计算过程中带来的电导卡滞、跃变以及失效等问题。模拟生物突触在刺激脉冲下的行为,能够更好地揭示电子器件的仿生特性机理并为高性能神经形态计算提供支撑。突触双脉冲易化是生物突触的重要特性,反映了在外界刺激作用下的易化和适应性过程,对揭示神经元的工作机制至关重要。为了构建突触双脉冲易化的模拟型忆阻器件,本研究通过器件的能带结构设计及氧空位缺陷态的调控,利用射频磁控溅射法制备了一种结构为Ag/FeOx/ITO的忆阻器。电学测试结果表明,该器件具有优异的渐进递增的非线性阻变特性,即模拟型阻变特性。在I-V循环扫描3000次范围内,这种器件均表现出模拟型阻变特性,可提供稳定的、可分离的16个电导状态,且在104 s内维持良好,说明这些电导状态是非易失性的,这主要归功于电子在氧空位缺陷态中的捕获与去捕获以及在势垒间隧穿行为。但是,在低电场强度情况下,捕获的热电子有可能会跃迁出浅陷阱能级,而呈现出易失性。根据这种器件的易失性和非易失性共存特性,通过调制电压脉冲宽度、幅度,器件能够表现出很好的突触双脉冲易化特性,显示出该类型器件在神经形态计算中的潜...     

忆阻器与神经元突触联系的分析

忆阻器作为"丢失的原件"被华裔科学家蔡少棠提出,是连接磁通和电荷的电学器件。忆阻器的可操控性和记忆功能,类似神经元细胞的性能。应用忆阻器代替现有晶体管的开关功能,是解决信号的通断智能控制的最理想办法,进而实现神经形态计算系统的智能控制。     

Mimicking Synaptic Plasticity and Neural Network Using Memtranstors

Artificial synaptic devices that mimic the functions of biological synapses have drawn enormous interest because of their potential in developing brain‐inspired computing. Current studies are focusing on memristive devices in which the change of the conductance state is used to emulate synaptic behaviors. Here, a new type of artificial synaptic devices based on the memtranstor is demonstrated, which is a fundamental circuit memelement in addition to the memristor, memcapacitor, and meminductor. The state of transtance (presented by the magnetoelectric voltage) in memtranstors acting as the synaptic weight can be tuned continuously with a large number of nonvolatile levels by engineering the applied voltage pulses. Synaptic behaviors including the long‐term potentiation, long‐term depression, and spiking‐time‐dependent plasticity are implemented in memtranstors made of Ni/0.7Pb(Mg_1/3Nb_2/3)O₃‐0.3PbTiO₃/Ni multiferroic heterostructures. Simulations reveal the capability of pattern learning in a memtranstor network. The work elucidates the promise of memtranstors as artificial synaptic devices with low energy consumption.     

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.     

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.     

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.     

Synergistic Gating of Electro‐Iono‐Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity

Emulation of brain‐like signal processing with thin‐film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di‐chalcogenide (MoS₂) neuristors are designed to mimic intracellular ion endocytosis–exocytosis dynamics/neurotransmitter‐release in chemical synapses using three approaches: (i) electronic‐mode: a defect modulation approach where the traps at the semiconductor–dielectric interface are perturbed; (ii) ionotronic‐mode: where electronic responses are modulated via ionic gating; and (iii) photoactive‐mode: harnessing persistent photoconductivity or trap‐assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge‐trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve “plasticity of plasticity–metaplasticity” via dynamic control of Hebbian spike‐time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures.     

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.     

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.     

Multilevel resistive switching and synaptic plasticity of nanoparticulated cobaltite oxide memristive device

Multilevel resistive switching(RS)is a key property to embrace the full potential of memristive devices for non-volatile memory and neuromorphic computing applications.In this study,we employed nanopar-ticulated cobaltite oxide(Co3O4)as a model material to demonstrate the multilevel RS and synaptic learning capabilities because of its multiple and stable redox state properties.The Pt/Co3O4/Pt memris-tive device exhibited tunable RS properties with respect to different voltages and compliance currents(CC)without the electroforming process.That is,the device showed voltage-dependent RS at a higher CC whereas CC-dependent RS was observed at lower CC.The device showed four different resistance states during endurance and retention measurements and non-volatile memory results indicated that the CC-based measurement had less variation.Besides,we investigated the basic and complex synap-tic plasticity properties using the analog current-voltage characteristics of the Pt/Co3O4/Pt device.In particular,we mimicked the potentiation-depression and four-spike time-dependent plasticity(STDP)rules such as asymmetric Hebbian,asymmetric anti-Hebbian,symmetric Hebbian,and symmetric anti-Hebbian learning rules.The results of the present work indicate that the cobaltite oxide is an excellent nanomaterial for both multilevel RS and neuromorphic computing applications.     

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.     

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.