光电忆阻器用于突触仿生领域的研究进展

doi:10.11896/cldb.22060027

材料导报

2024, Vol. 38

Issue (4): 22060027-10 https://doi.org/10.11896/cldb.22060027

无机非金属及其复合材料

光电忆阻器用于突触仿生领域的研究进展

刘菁, 张建, 赵波^*

江苏师范大学物理与电子工程学院,江苏徐州 221116

Progress in the Use of Optoelectronic Memristors for Synaptic Bionics

LIU Jing, ZHANG Jian, ZHAO Bo^*

School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou 221116, Jiangsu, China

摘要
参考文献
相关文章
推荐阅读
Metrics

下载:

全文 ( PDF ) ( 29578KB )
输出: BibTeX | EndNote (RIS)

摘要存算分离的传统计算模式已经无法满足信息爆炸时代对大数据处理的需求,因此,基于类神经网络的神经形态计算被广泛应用于人工智能的研究。随着集成技术的进步,新形态硬件系统开始进入大众视野,忆阻器作为新兴的除电容、电感和电阻之外的第四种基本电路元件,综合了光电子学、半导体科学等多领域的优点,能够为神经形态计算硬件化提供新的思路。本文首先简述了光电忆阻器件的基本结构、机制和脉冲时间依赖可塑性等类生物突触的功能;其次介绍了四种光电型和全光型忆阻器件;然后综述了光电忆阻器件的类脑特性,如模拟巴甫洛夫经典实验和味觉厌恶过程的联想式学习、习惯化和敏化模式的非联想式学习、具有“学习-遗忘-再学习”特征的经验学习,以及结合人工神经网络实现图像记忆、处理和识别等仿生功能;最后总结了光电忆阻器件在突触仿生领域所面临的挑战,并展望其在神经形态计算方向的广阔应用前景。

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刘菁
	张建
	赵波

关键词: 光电忆阻器生物突触类脑特性神经网络

Abstract: The traditional computing model has been unable to meet the needs of big data processing in the era of information explosion. Therefore, neuromorphic computing based on neural networks is widely used in artificial intelligence research to address those needs. With the advancements in integration technology, there has been an increased focus on the development of new hardware systems. The memristor, which is the fourth fundamental circuit element in addition to capacitors, inductors, and resistors, combines the benefits of optoelectronics, semiconductor science, and other fields, and can present novel opportunities for the hardware used in the field of neuromorphic computing. In this paper, the fundamental concepts of optoelectronic memristors and functions of biological synapses, including pulse-time-dependent plasticity, are presented. In addition, four categories of optoelectronic and fully optical memristors are studied. Subsequently, the brain-like properties of optoelectronic memristors are discussed, including associative learning, non-associative learning, and experiential learning. Associative learning simulates Pavlov’s classic experiment and taste aversion, while non-associative learning comprises habituation and sensitization. Moreover, image memorization, image processing, and pattern recognition based on memristor devices have been realized using artificial neural networks. Finally, the challenges and prospects for synaptic bionics and neuromorphic computing are summarized.

Key words: optoelectronic memristor biological synapse brain-like property artificial neural network

出版日期: 2024-02-25 发布日期: 2024-03-01

ZTFLH:

TN361

基金资助: 徐州市基础研究计划项目(KC22007)

通讯作者: *赵波,江苏师范大学物理与电子工程学院教授、硕士研究生导师。2009年上海交通大学博士毕业。研究方向为纳米电子材料和器件,主要从事电子器件互连、纳米复合材料、纳米传感器等方面的工作。研制出突触仿生器件、呼吸传感器、平板显示器等多种纳米器件,主持和参与国家自然科学基金、江苏省自然科学基金和省高校基金等多项课题,在国内外期刊发表论文30多篇,申请发明专利10余项。phyzhaobo@jsnu.edu.cn

作者简介: 刘菁,现为江苏师范大学物理与电子工程学院硕士研究生,在赵波老师的指导下进行研究,目前的研究领域为纳米电子材料与器件。

引用本文:

刘菁, 张建, 赵波. 光电忆阻器用于突触仿生领域的研究进展[J]. 材料导报, 2024, 38(4): 22060027-10.
LIU Jing, ZHANG Jian, ZHAO Bo. Progress in the Use of Optoelectronic Memristors for Synaptic Bionics. Materials Reports, 2024, 38(4): 22060027-10.

链接本文:

http://www.mater-rep.com/CN/10.11896/cldb.22060027 或 http://www.mater-rep.com/CN/Y2024/V38/I4/22060027

1 Qiu C Y, Li L, Zhang H, et al. Computer Science, 2018, 45(11A), 71(in Chinese).
邱赐云, 李礼, 张欢, 等. 计算机科学, 2018, 45(11A), 71.
2 Wu J Q, Lai F. Microelectronics, 2020, 50(3), 384(in Chinese).
武俊齐, 赖凡. 微电子学, 2020, 50(3), 384.
3 Qin S, Wang F, Liu Y, et al. 2D Materials, 2017, 4(3), 035022.
4 Zhou X G. Research of memelements in metal oxide/graphene composite film. Master’s Thesis, Shanghai Normal University, China, 2021(in Chinese).
周心葛. 金属氧化物/石墨烯复合薄膜在记忆元件中的应用研究. 硕士学位论文, 上海师范大学, 2021.
5 Qin S, Liu Y, Wang X, et al. In: Lasers & Electro-optics. San Jose CA, USA, 2016, pp. SW1R. 4.
6 Zhang Z C, Li Y, Wang J J, et al. Nano Research, 2021, 14(12), 4591.
7 Liu Y C, Lin Y, Wang Z Q, et al. Acta Physica Sinica, 2019, 68(16), 168504(in Chinese).
刘益春, 林亚, 王中强, 等. 物理学报, 2019, 68(16), 168504.
8 Chen Y. Construction of memristive devices based on tungsten oxide/silver composite films and research on synaptic bionics. Master’s Thesis, Northeast Normal University, China, 2021(in Chinese).
陈颖. 基于氧化钨/银复合薄膜的忆阻器件构筑及其神经突触仿生研究. 硕士学位论文, 东北师范大学, 2021.
9 Cho S W, Jo C, Kim Y H, et al. Nano-micro Letters, 2022, 14(1), 203.
10 Hu D C, Yang R, Jiang L, et al. ACS Applied Materials & Interfaces, 2018, 10(7), 6463.
11 Zhu X, Lu W D. ACS Nano, 2018, 12(2), 1242.
12 Maier P, Hartmann F, Rebello S D M, et al. Applied Physics Letters, 2016, 109(2), 023501.
13 Jaafar A H, Kemp N T. Carbon, 2019, 153, 81.
14 Ham S, Choi S, Cho H, et al. Advanced Functional Materials, 2019, 29(5), 1806646.
15 Zhong W M, Tang X G, Liu Q X, et al. Materials & Design, 2022, 222, 111046.
16 Hu L, Yang J, Wang J, et al. Advanced Functional Materials, 2021, 31(4), 2005582.
17 Shan X, Zhao C, Wang X, et al. Advanced Science, 2022, 9(6), 2104632.
18 Yang J, Hu L, Shen L, et al. Fundamental Research, 2024, 4(1), 158.
19 Li H, Jiang X, Ye W, et al. Nano Energy, 2019, 65, 104000.
20 Ahmed T, Kuriakose S, Mayes E, et al. Small, 2019, 15(22), 1900966.
21 John R A, Liu F, Chien N A, et al. Advanced Materials, 2018, 30(25), 1800220.
22 Liu L. Study on the resistive switching and the application of artificial synapse of HfO₂/BiFeO₃ and two-dimensional TiS₃ memristors. Master’s Thesis, Hubei University, China, 2021(in Chinese).
刘磊. 基于HfO₂/BiFeO₃及二维TiS₃忆阻器的阻变特性及突触仿生研究. 硕士学位论文, 湖北大学, 2021.
23 Hou Y X, Li Y, Zhang Z C, et al. ACS Nano, 2021, 15(1), 1497.
24 Yin L, Han C, Zhang Q, et al. Nano Energy, 2019, 63, 103859.
25 He H K, Yang R, Zhou W, et al. Small, 2018, 14(15), 1800079.
26 Kumar M, Kim H S, Kim J. Advanced Materials, 2019, 31(19), 1900021.
27 Zhou L, Zhang S R, Yang J Q, et al. Nanoscale, 2020, 12(3), 1484.
28 Gong G, Gao S, Xie Z, et al. Nanoscale, 2021, 13(2), 1029.
29 Zhao B, Xiao M, Shen D, et al. Nanotechnology, 2020, 31(12), 125201.
30 Li D, Li C, Ilyas N, et al. Advanced Intelligent Systems, 2020, 2(11), 2000107.
31 Li L, Wang X L, Pei J, et al. Science China Materials, 2021, 64(5), 1219.
32 Han X, Xu Z, Wu W, et al. Small Structures, 2020, 1(3), 2000029.
33 Chen S, Lou Z, Chen D, et al. Advanced Materials, 2018, 30(7), 1705400.
34 Sun F, Lu Q, Liu L, et al. Advanced Materials Technologies, 2019, 5(1), 1900888.
35 Liang J B. Preparation of graphene oxide-based composite memristive materials and research on its photoelectric memristive characteristics. Master’s Thesis, Northeast Normal University, China, 2021(in Chinese).
梁佳冰. 氧化石墨烯基复合忆阻材料的制备及其光电忆阻特性研究. 硕士学位论文, 东北师范大学, 2021.
36 Zhou F, Zhou Z, Chen J, et al. Nature Nanotechnology, 2019, 14(8), 776.
37 Shen C, Gao X, Chen C, et al. Nanotechnology, 2022, 33(6), 065205.
38 Seo S, Jo S H, Kim S, et al. Nature Communications, 2018, 9(1), 5106.

[1]	刘凯伟, 刘琦牮, 李骏, 余映红, 卿新林. 基于FBG传感器和卷积神经网络的复合材料结构载荷识别研究[J]. 材料导报, 2023, 37(1): 21040290-7.
[2]	杜青铉, 张宇航, 孙伟豪, 刘蕊, 庄尧量, 夏军武. 基于混合模型的煤矸石透水混凝土透水系数预测[J]. 材料导报, 2022, 36(Z1): 22040077-5.
[3]	林志玮, 赵兴科, 赵增磊, 王世泽. 脉冲激光热爆箔法制备金属粉末试验及工艺优化[J]. 材料导报, 2022, 36(Z1): 21080257-6.
[4]	梁李斯, 郭文龙, 马洪月, 弥晗, 张宇, 米嘉毓, 李林波. 多孔吸声材料的吸声性能预测及吸声模型研究进展[J]. 材料导报, 2022, 36(23): 21030180-8.
[5]	杨传礼, 张修庆. 基于机器视觉和深度学习的材料缺陷检测应用综述[J]. 材料导报, 2022, 36(16): 20070136-9.
[6]	张永志, 李旭英, 辛全忠, 孔祥明, 王永亮. 自组织CNN建模识别耐热钢金相组织研究[J]. 材料导报, 2022, 36(12): 21030032-6.
[7]	张永志, 辛全忠, 王永亮, 孔祥明, 刘昉, 杨再胜. 基于迁移学习的钢金相组织分类与识别方法的研究[J]. 材料导报, 2021, 35(24): 24152-24157.
[8]	黄炜, 葛培, 李萌, 许洪飞. 混杂纤维再生砖骨料混凝土正交试验及卷积神经网络预测分析[J]. 材料导报, 2021, 35(19): 19022-19029.
[9]	黄炜, 周烺, 葛培, 杨涛. 基于PSO-BP和GA-BP神经网络再生砖骨料混凝土强度模型的对比研究[J]. 材料导报, 2021, 35(15): 15026-15030.
[10]	康靓, 米晓希, 王海莲, 吴璐, 孙凯, 汤爱涛. 人工神经网络在材料科学中的研究进展[J]. 材料导报, 2020, 34(21): 21172-21179.
[11]	刘博, 王社良, 何露, 李昊, 杨涛, 李彬彬. NiTi形状记忆合金丝的约束回复应力输出特性及本构模型[J]. 材料导报, 2020, 34(10): 10082-10087.
[12]	陆骐峰,孙富钦,王子豪,张珽. 柔性人工突触:面向智能人机交互界面和高效率神经网络计算的基础器件[J]. 材料导报, 2020, 34(1): 1022-1049.
[13]	杨学平, 王正江, 蒋超宇, 薛秀丽. 基于BP神经网络法研究锂电池荷电状态[J]. 材料导报, 2019, 33(Z2): 53-55.
[14]	李地红, 高群, 夏娴, 张景卫, 于海洋, 王艳君, 代函函, 许国栋. 基于BP神经网络的混凝土综合性能预测[J]. 材料导报, 2019, 33(Z2): 317-320.
[15]	王仕发,李丹明,肖玉华,杨震春,李居平,郝剑,杨长青. 用于空间辐射环境探测的金刚石探测器研究综述[J]. 《材料导报》期刊社, 2018, 32(9): 1459-1468.

[1]	Yanzhen WANG, Mingming CHEN, Chengyang WANG. Preparation and Electrochemical Properties Characterization of High-rate SiO₂/C Composite Materials[J]. Materials Reports, 2018, 32(3): 357 -361 .
[2]	Yimeng XIA, Shuai WU, Feng TAN, Wei LI, Qingmao WEI, Chungang MIN, Xikun YANG. Effect of Anionic Groups of Cobalt Salt on the Electrocatalytic Activity of Co-N-C Catalysts[J]. Materials Reports, 2018, 32(3): 362 -367 .
[3]	Qingshun GUAN,Jian LI,Ruyuan SONG,Zhaoyang XU,Weibing WU,Yi JING,Hongqi DAI,Guigan FANG. A Survey on Preparation and Application of Aerogels Based on Nanomaterials[J]. Materials Reports, 2018, 32(3): 384 -390 .
[4]	Lijing YANG,Zhengxian LI,Chunliang HUANG,Pei WANG,Jianhua YAO. Producing Hard Material Coatings by Laser-assisted Cold Spray:a Technological Review[J]. Materials Reports, 2018, 32(3): 412 -417 .
[5]	Zhiqiang QIAN,Zhijian WU,Shidong WANG,Huifang ZHANG,Haining LIU,Xiushen YE,Quan LI. Research Progress in Preparation of Superhydrophobic Coatings on Magnesium Alloys and Its Application[J]. Materials Reports, 2018, 32(1): 102 -109 .
[6]	Wen XI,Zheng CHEN,Shi HU. Research Progress of Deformation Induced Localized Solid-state Amorphization in Nanocrystalline Materials[J]. Materials Reports, 2018, 32(1): 116 -121 .
[7]	Xing LIANG, Guohua GAO, Guangming WU. Research Development of Vanadium Oxide Serving as Cathode Materials for Lithium Ion Batteries[J]. Materials Reports, 2018, 32(1): 12 -33 .
[8]	Hao ZHANG,Yongde HUANG,Yue GUO,Qingsong LU. Technological and Process Advances in Robotic Friction Stir Welding[J]. Materials Reports, 2018, 32(1): 128 -134 .
[9]	Laima LUO, Mengyao XU, Xiang ZAN, Xiaoyong ZHU, Ping LI, Jigui CHENG, Yucheng WU. Progress in Irradiation Damage of Tungsten and Tungsten AlloysUnder Different Irradiation Particles[J]. Materials Reports, 2018, 32(1): 41 -46 .
[10]	Fengsen MA,Yan YU,Jie ZHANG,Haibo CHEN. A State-of-the-art Review of Cytotoxicity Evaluation of Biomaterials[J]. Materials Reports, 2018, 32(1): 76 -85 .

Viewed

Full text

Abstract

Cited

Shared

Discussed