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。

Sparse coding is a prevalent method for image inpainting and feature extraction, which can repair corrupted images or improve data processing efficiency, and has numerous applications in computer vision and signal processing. Recently, several memristor-based in-memory computing systems have been proposed to enhance the efficiency of sparse coding remarkably. However, the variations and low precision of the devices will deteriorate the dictionary, causing inevitable degradation in the accuracy and reliability of the application. In this work, a digital-analog hybrid memristive sparse coding system is proposed utilizing a multilevel Pt/Al₂O₃/AlO_x/W memristor, which employs the forward stagewise regression algorithm: The approximate cosine distance calculation is conducted in the analog part to speed up the computation, followed by high-precision coefficient updates performed in the digital portion. We determine that four states of the aforementioned memristor are sufficient for the processing of natural images. Furthermore, through dynamic adjustment of the mapping ratio, the precision requirement for the digit-to-analog converters can be reduced to 4 bits. Compared to the previous system, our system achieves higher image reconstruction quality of the 38 dB peak-signal-to-noise ratio. Moreover, in the context of image inpainting, images containing 50% missing pixels can be restored with a reconstruction error of 0.0424 root-mean-squared error.

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/FeO_x/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力和优势。

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。

With rapid advancement and deep integration of artificial intelligence and the internet-of-things, artificial intelligence of things has emerged as a promising technology changing people’s daily life. Massive growth of data generated from the devices challenges the AIoT systems from information collection, storage, processing and communication. In the review, we introduce volatile threshold switching memristors, which can be roughly classified into three types: metallic conductive filament-based TS devices, amorphous chalcogenide-based ovonic threshold switching devices, and metal-insulator transition based TS devices. They play important roles in high-density storage, energy efficient computing and hardware security for AIoT systems. Firstly, a brief introduction is exhibited to describe the categories (materials and characteristics) of volatile TS devices. And then, switching mechanisms of the three types of TS devices are discussed and systematically summarized. After that, attention is focused on the applications in 3D cross-point memory technology with high storage-density, efficient neuromorphic computing, hardware security (true random number generators and physical unclonable functions), and others (steep subthreshold slope transistor, logic devices,etc.). Finally, the major challenges and future outlook of volatile threshold switching memristors are presented.