二维忆阻材料及其阻变机理研究进展

二维材料由于具有超薄、柔性的层状结构,有望突破传统阻变材料难以降低忆阻器尺寸的限制,成为存储器、柔性电子、神经形态计算等领域的研究热点。本文从器件结构、材料种类、开关机理、电极和功能层改性等方面综述和分析了近年来二维材料基忆阻器的研究进展。“三明治”结构是忆阻器最常用的结构,通过插入调节层可提高器件稳定性;平面结构可操控性较差,但其独特的易观察性为研究忆阻器的阻变机理提供了有力工具。石墨烯及其衍生物和二硫化钼忆阻器阻变性能较好且应用广泛;二硫化钨、碲化钼、六方氮化硼、黑磷、MXene、二维钙钛矿等也逐渐被应用于忆阻器,但性能仍需优化。器件开关机制主要包括导电细丝、电荷俘获与释放、原子空位等。选择功函数合适的电极,可有效调控界面势垒和载流子输运;通过将二维材料与聚合物复合或掺杂纳米粒子,可有效降低器件的离散性。下一步应从界面性质精确控制和耐弯曲耐极端温度等方面深入研究,为新型二维材料忆阻器的工业化应用奠定基础。     

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。     

本征可拉伸阈值型忆阻器及其神经元仿生特性

研制具有生物神经元信息功能的柔性电子器件对于发展智能穿戴技术具有重要意义。传统阈值型忆阻器可模仿神经元信息整合功能,但因缺乏本征柔韧性,难以满足应用需求。本工作制备了一种基于本征可拉伸阈值型忆阻器的柔性人工神经元,它由银纳米线–聚氨酯复合介质薄膜和液态金属电极构成。在外加电压下,器件呈现良好的阈值电阻转变特性,这归因于银纳米线间形成非连续银导电细丝的动态通断。该器件可模仿生物神经元的信息整合–发放及脉冲强度和脉冲间隔调制的尖峰放电功能。在20%拉伸应变下,器件工作参数基本保持稳定,性能未发生明显退化。本工作为发展可拉伸柔性人工神经元及下一代智能穿戴设备提供重要材料和技术参考。     

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。     

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。     

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。     

基于忆阻器交叉阵列的变步长sign-sign LMS算法硬件实现

基于忆阻器交叉阵列结构,提出变步长的sign-sign LMS自适应滤波器算法及其一种相应的硬件实现电路。在该电路中,一方面继承原定步长sign-sign LMS忆阻器交叉阵列电路[1]的诸多优点,另一方面经过仿真发现该变步长sign-sign LMS在保持高精度的前提下,能够实现快速的收敛。     

铁修饰二碲化钼吸附氮化物的气敏特性研究

本工作采用基于密度泛函理论的第一性原理计算方法,研究了毒害性气体NO、NO₂和NH₃在铁修饰MoTe₂(Fe-MoTe₂)单分子层上的吸附和传感行为,探究其作为电阻型化学气体传感器的潜力。首先,研究了Fe修饰在单层MoTe₂上最稳定的几何构型和电子行为。结果表明,Fe原子掺杂剂可以稳定地吸附在单层MoTe₂表面T_Mo处,修饰后体系的带隙减小并且电子密度增加,产生2.00μ_B磁矩。其次,Fe-MoTe₂对NO、NO₂和NH₃气体的吸附能分别达到了-3.13 eV、-2.27 eV和-1.19 eV,总态密度图(DOS)以及分波态密度图(PDOS)的分析验证了Fe原子修饰对气体吸附性能的影响。能带结构和差分电荷密度分析为Fe-MoTe₂作为电阻型化学气体传感器提供了基本传感机理。最后,灵敏度分析表明Fe-MoTe₂     

忆阻器在神经突触仿生中的应用研究进展

随着对计算机性能要求的不断提高,人们一直在寻找能像人脑一样具有学习记忆功能的新型计算机。自从2008年惠普实验室发现忆阻器以后,发展具有人脑水平的智能计算机成为可能。众所周知,突触是大脑神经网络的基本单元,突触可塑性是学习和记忆的生物学基础。因此,为了实现具有学习和记忆功能的智能计算机,利用忆阻器模拟突触可塑性至关重要。综述了忆阻器在模拟突触的增强、抑制、短时程可塑性和长时程可塑性方面的研究现状,并对其研究前景进行了展望。     

硒化锌量子点二维阵列的LB方法制备与表征

以去铁铁蛋白作为生物模板合成了硒化锌量子点,采用Langmuir-Blodgett技术在硅基质表面制备1:4的EA/SMA的LB薄膜并将硒化锌核铁蛋白吸附组装到EA/SMA薄膜上形成二维阵列。用紫外-可见吸收光谱(UV-Vis)、荧光光谱(PL)、傅里叶转换红外光谱(FTIR)、扫描电子显微镜(SEM)、透射电子显微镜(TEM)进行表征,结果表明成功制备得到了硒化锌量子点核铁蛋白单分子层二维阵列。这种排列在固体表面的有序微观尺寸的纳米结构由于其独特的光学和生物学特性在生物传感、药物和诊断等领域具有潜在的应用前景。     

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.     

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.     

Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering

Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.     

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.     

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.     

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.     

Highly Active,Durable Ultrathin MoTe2 Layers for the Electroreduction of CO2 to CH4

The electroreduction of CO₂ to CH₄ is a highly desirable, challenging research topic. In this study, an electrocatalytic system comprising ultrathin MoTe₂ layers and an ionic liquid electrolyte for the reduction of CO₂ to methane is reported, efficiently affording methane with a faradaic efficiency of 83 ± 3% (similar to the best Cu‐based catalysts reported thus far) and a durable activity of greater than 45 h at a relatively high current density of 25.6 mA cm^?2 (?1.0 V_RHE). The results obtained can facilitate research on the design of other transition‐metal dichalcogenide electrocatalysts for the reduction of CO₂ to valuable fuels.     

Flexible Neuromorphic Electronics for Computing,Soft Robotics,and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.