本征可拉伸阈值型忆阻器及其神经元仿生特性

doi:10.15541/jim20220712

无机材料学报 ›› 2023, Vol. 38 ›› Issue (4): 413-420.DOI: 10.15541/jim20220712

• 专栏：神经形态材料与器件(特邀编辑：万青) • 上一篇下一篇

本征可拉伸阈值型忆阻器及其神经元仿生特性

田雨¹^,²(), 朱小健²(), 孙翠², 叶晓羽², 刘慧媛², 李润伟²

1.宁波大学材料科学与化学工程学院, 宁波 315211
2.中国科学院宁波材料技术与工程研究所, 宁波 315201

收稿日期:2022-11-28 修回日期:2022-12-17 出版日期:2023-04-20 网络出版日期:2022-12-28
通讯作者: 朱小健, 研究员. E-mail: zhuxj@nimte.ac.cn
作者简介:田雨(1997-), 男, 硕士研究生. E-mail: tianyu@nimte.ac.cn
基金资助:
国家自然科学基金(62174164);国家自然科学基金(61974179);国家自然科学基金(92064011);宁波市自然科学基金(202003N4029);中国科学院科研仪器设备研制项目(YJKYYQ20200030);中国科学院对外合作重点项目(174433KYSB20190038)

Intrinsically Stretchable Threshold Switching Memristor for Artificial Neuron Implementations

TIAN Yu¹^,²(), ZHU Xiaojian²(), SUN Cui², YE Xiaoyu², LIU Huiyuan², LI Runwei²

1. School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
2. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China

Received:2022-11-28 Revised:2022-12-17 Published:2023-04-20 Online:2022-12-28
Contact: ZHU Xiaojian, professor. E-mail: zhuxj@nimte.ac.cn
About author:TIAN Yu (1997-), male, Master candidate. E-mail: tianyu@nimte.ac.cn
Supported by:
National Natural Science Foundation of China(62174164);National Natural Science Foundation of China(61974179);National Natural Science Foundation of China(92064011);Ningbo Natural Science Foundation(202003N4029);Scientific Instrument Developing Project of the Chinese Academy of Sciences(YJKYYQ20200030);External Cooperation Program of Chinese Academy of Sciences(174433KYSB20190038)

摘要/Abstract

摘要：

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。

关键词: 神经形态计算, 忆阻器, 阈值开关, 可拉伸, 人工神经元

Abstract:

The exploration of flexible electronic devices with information processing functions of biological neurons is of great significance for the development of intelligent wearable technologies. Due to lack of inherent mechanical flexibility, conventional threshold-switching memristor based on rigid materials that can implement the computing functions of biological neurons is difficult to fulfill the requirements for potential applications in the future. In this work, an intrinsically stretchable threshold-switching memristor was prepared by using silver nanowire-polyurethane composite as the dielectric layer and liquid metal as the electrodes, respectively. Under application of a sweeping voltage, the device exhibited reliable threshold switching characteristics, which was switched from the high resistance state (HRS) to the low resistance state (LRS) during device programming and spontaneously relaxed to the HRS upon voltage application. Further analysis shows that the underlying mechanism can be attributed to the dynamic formation and rupture of discontinuous silver conductive filaments formed between silver nanowires. In the pulse programming mode, memristor device is able to emulate the integration and firing characteristics of biological neurons, suggesting its great potential as an artificial neuron. Moreover, the pulse amplitude and pulse interval modulated neuronal spiking behaviors are successfully replicated using such devices. Under 20% tensile strain, the threshold-switching memristor shows negligible changes in the operating parameters during device switching and neuronal function implementations, suggesting its excellent mechanical flexibility and stability. This work provides important guidelines for the development of high-performance stretchable artificial neuronal devices and next-generation intelligent wearable systems.

Key words: neuromorphic computing, memristor, threshold switching, stretchable, artificial neuron

中图分类号:

TN389

田雨, 朱小健, 孙翠, 叶晓羽, 刘慧媛, 李润伟. 本征可拉伸阈值型忆阻器及其神经元仿生特性[J]. 无机材料学报, 2023, 38(4): 413-420.

TIAN Yu, ZHU Xiaojian, SUN Cui, YE Xiaoyu, LIU Huiyuan, LI Runwei. Intrinsically Stretchable Threshold Switching Memristor for Artificial Neuron Implementations[J]. Journal of Inorganic Materials, 2023, 38(4): 413-420.

图/表 7

图1 液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件的制备流程图

Fig. 1 Flow chart of Cu@GaIn/AgNWs-PU/Cu@GaIn device fabrication

图2 液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件的I-V特性

Fig. 2 I-V characteristics of the Cu@GaIn/AgNWs-PU/Cu@GaIn device (a) I-V curve of the Cu@GaIn/AgNWs-PU/Cu@GaIn device; (b) Cumulative distribution function of the operation voltages; (c) I-V curves of the device under different compliance currents; (d) Dependence of the operation voltage on the thickness of the AgNWs-PU film

图3 液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件的工作机制

Fig. 3 Working mechanism of the Cu@GaIn/AgNWs-PU/Cu@GaIn device (a) Dependence of the device resistance at the LRS on the compliance currents; (b) Illustration of the dynamic Ag filament formation/rupture between AgNWs during threshold switching process

图4 液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件模拟生物神经元的整合发放功能

Fig. 4 Emulation of the integrate-and-fire behaviors of biological neurons with the Cu@GaIn/AgNWs-PU/Cu@GaIn device (a, b) Schematic diagram for (a) biological neuron and (b) artificial neuron; (c) Typical integrate-and-fire behavior of the memristor based artificial neuron; Colorful figures are available on website

图5 输入脉冲幅值、间隔对阈值型忆阻人工神经元整合发放功能的影响

Fig. 5 Influences of the voltage pulse amplitude and interval on the integrate-and-fire behaviors of the memristor based artificial neuron (a) Integrate-and-fire behaviors of the device as a function of the voltage pulse amplitude with pulse interval and width at 30 ms and 10 ms, respectively; (b) Relationship between the required pulse number for device firing (NFire) and the pulse amplitude; (c) Integrate-and-fire behaviors of the device as a function of the voltage pulse interval with pulse amplitude and width at 6 V and 10 ms, respectively; (d) Relationship between the required pulse number for device firing (NFire) and the pulse interval; Colorful figures are available on website

图6 拉伸应变条件下液态金属/银纳米线-聚氨酯复合薄膜/液态金属器件的阈值转变电压研究

Fig. 6 Threshold switching voltages of the Cu@GaIn/AgNWs-PU/Cu@GaIn device under different tensile strain conditions (a) Schematics of the device stretching under tensile strain; (b) Optical images of the device before and after being stretched by 20%; (c, d) Evolution of the operation voltage for the device with tensile strain in (c) x and (d) y directions

图7 拉伸应变条件下人工神经元的整合发放功能测试

Fig. 7 Integrate-and-fire function test of artificial neuron under tensile strain conditions (a) Control pulse interval (30 ms), width (10 ms) and amplitude (6 V) being unchanged, and the NFire change of the device by changing tensile strain ratio of the device in the x direction; (b) Evolution of NFire of the device with tensile strain ratio

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本征可拉伸阈值型忆阻器及其神经元仿生特性

Intrinsically Stretchable Threshold Switching Memristor for Artificial Neuron Implementations

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