Memristor's characteristics: From non-ideal to ideal

doi:10.1088/1674-1056/ac7548

Abstract Memristor has been widely studied in the field of neuromorphic computing and is considered to be a strong candidate to break the von Neumann bottleneck. However, the non-ideal characteristics of memristor seriously limit its practical application. There are two sides to everything, and memristors are no exception. The non-ideal characteristics of memristors may become ideal in some applications. Genetic algorithm (GA) is a method to search for the optimal solution by simulating the process of biological evolution. It is widely used in the fields of machine learning, combinatorial optimization, and signal processing. In this paper, we simulate the biological evolutionary behavior in GA by using the non-ideal characteristics of memristors, based on which we design peripheral circuits and path planning algorithms based on memristor networks. The experimental results show that the non-ideal characteristics of memristor can well simulate the biological evolution behavior in GA.

Keywords: memristor non-ideal characteristic genetic algorithm path planning

Received: 02 May 2022
Revised: 23 May 2022
Accepted manuscript online: 02 June 2022

PACS:	84.35.+i	(Neural networks)
	84.37.+q	(Measurements in electric variables (including voltage, current, resistance, capacitance, inductance, impedance, and admittance, etc.))
	87.19.lv	(Learning and memory)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61976246 and U20A20227), the Natural Science Foundation of Chongqing, China (Grant No. cstc2020jcyj-msxm X0385), and the National Key R&D Program of China (Grant Nos. 2018YFB130660 and 2018YFB1306604).

Corresponding Authors: Xiaofang Hu
E-mail: huxf@swu.edu.cn

Cite this article:

Fan Sun(孙帆), Jing Su(粟静), Jie Li(李杰), Shukai Duan(段书凯), and Xiaofang Hu(胡小方) Memristor's characteristics: From non-ideal to ideal 2023 Chin. Phys. B 32 028401

[1] Chua L 1971 IEEE Trans. Circuit Theory 18 507
[2] Strukov D B, Snider G S, Stewart D R and Williams R S 2008 Nature 453 80
[3] Yoon J H, Wang Z, Kim K M, Wu H, Ravichandran V, Xia Q, Hwang C S and Yang J J 2018 Nature 9 417
[4] Burr G W, Shelby R M, Sebastian A, et al. 2017 Adv. Phys. X 2 89
[5] Yu S, Gao B, Fang Z, Yu H, Kang J and Wong H P 2013 Adv. Mater. 25 1774
[6] Prezioso M, Merrikh-Bayat F, Hoskins B D, Adam G C, Likharev K K and Strukov D B 2015 Nature 521 61
[7] Li C, Belkin D, Li Y, et al. 2018 2018 IEEE International Memory Workshop (IMW), May 13-16, 2018, Kyoto, Japan, pp. 12-15
[8] Jo S H, Chang T, Ebong I, Bhadviya B B, Mazumder P and Lu W 2010 Nano Lett. 10 1297
[9] Yao P, Wu H, Gao B, Tang J, Zhang Q, Zhang W, Yang J J and Qian H 2020 Nature 577 641
[10] Chen L, He Z, Li C, Wen S and Chen Y 2020 Int. J. Bifur. Chaos 30 2050172
[11] Xi Y, Gao B, Tang J, Chen A, Chang M F, Hu X S, Van Der Spiegel J, Qian H and Wu H 2020 Proc. IEEE 109 14
[12] Yao P, Wu H, Gao B, et al. 2017 Nat. Commun. 8 15199
[13] Cai Y, Tang T, Xia L, Cheng M, Zhu Z, Wang Y and Yang H 2018 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC) January 22-25, 2018, Jeju Island, South Korea, pp. 117-122
[14] Ambrogio S, Narayanan P, Tsai H, et al. 2018 Nature 558 60
[15] Li C, Li Y, Jiang H, et al. 2018 2018 IEEE International Symposium on Circuits and Systems (ISCAS), May 27-30, 2018, Florence, Italy, pp. 1-4
[16] Ravichandran V, Li C, Banagozar A, Yang J J and Xia Q 2018 Sci. China Inf. Sci. 61 1
[17] Wang Y, Wu S, Tian L and Shi L 2020 Neurocomputing 407 270
[18] Katoch S, Chauhan S S and Kumar V 2021 Multimedia Tools and Applications 80 8091
[19] Mirjalili S, Song D J, Sadiq A S and Faris H 2020 Nature-Inspired Optimizers Vol. 811 p. 69
[20] Zhi H and Liu S 2019 J. Visual Commun. Image Represent. 58 495
[21] Campbell K A 2017 Microelectron. J. 59 10

[1]	Adaptive genetic algorithm-based design of gamma-graphyne nanoribbon incorporating diamond-shaped segment with high thermoelectric conversion efficiency Jingyuan Lu(陆静远), Chunfeng Cui(崔春凤), Tao Ouyang(欧阳滔), Jin Li(李金), Chaoyu He(何朝宇), Chao Tang(唐超), and Jianxin Zhong(钟建新). Chin. Phys. B, 2023, 32(4): 048401.
[2]	Hopf bifurcation and phase synchronization in memristor-coupled Hindmarsh-Rose and FitzHugh-Nagumo neurons with two time delays Zhan-Hong Guo(郭展宏), Zhi-Jun Li(李志军), Meng-Jiao Wang(王梦蛟), and Ming-Lin Ma(马铭磷). Chin. Phys. B, 2023, 32(3): 038701.
[3]	Memristor hyperchaos in a generalized Kolmogorov-type system with extreme multistability Xiaodong Jiao(焦晓东), Mingfeng Yuan(袁明峰), Jin Tao(陶金), Hao Sun(孙昊), Qinglin Sun(孙青林), and Zengqiang Chen(陈增强). Chin. Phys. B, 2023, 32(1): 010507.
[4]	High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors Xiao-Juan Lian(连晓娟), Jin-Ke Fu(付金科), Zhi-Xuan Gao(高志瑄),Shi-Pu Gu(顾世浦), and Lei Wang(王磊). Chin. Phys. B, 2023, 32(1): 017304.
[5]	Firing activities in a fractional-order Hindmarsh-Rose neuron with multistable memristor as autapse Zhi-Jun Li(李志军), Wen-Qiang Xie(谢文强), Jin-Fang Zeng(曾金芳), and Yi-Cheng Zeng(曾以成). Chin. Phys. B, 2023, 32(1): 010503.
[6]	High throughput N-modular redundancy for error correction design of memristive stateful logic Xi Zhu(朱熙), Hui Xu(徐晖), Weiping Yang(杨为平), Zhiwei Li(李智炜), Haijun Liu(刘海军), Sen Liu(刘森), Yinan Wang(王义楠), and Hongchang Long(龙泓昌). Chin. Phys. B, 2023, 32(1): 018502.
[7]	Characteristics of piecewise linear symmetric tri-stable stochastic resonance system and its application under different noises Gang Zhang(张刚), Yu-Jie Zeng(曾玉洁), and Zhong-Jun Jiang(蒋忠均). Chin. Phys. B, 2022, 31(8): 080502.
[8]	Fabrication and investigation of ferroelectric memristors with various synaptic plasticities Qi Qin(秦琦), Miaocheng Zhang(张缪城), Suhao Yao(姚苏昊), Xingyu Chen(陈星宇), Aoze Han(韩翱泽),Ziyang Chen(陈子洋), Chenxi Ma(马晨曦), Min Wang(王敏), Xintong Chen(陈昕彤), Yu Wang(王宇),Qiangqiang Zhang(张强强), Xiaoyan Liu(刘晓燕), Ertao Hu(胡二涛), Lei Wang(王磊), and Yi Tong(童祎). Chin. Phys. B, 2022, 31(7): 078502.
[9]	Pulse coding off-chip learning algorithm for memristive artificial neural network Ming-Jian Guo(郭明健), Shu-Kai Duan(段书凯), and Li-Dan Wang(王丽丹). Chin. Phys. B, 2022, 31(7): 078702.
[10]	Design and FPGA implementation of a memristor-based multi-scroll hyperchaotic system Sheng-Hao Jia(贾生浩), Yu-Xia Li(李玉霞), Qing-Yu Shi(石擎宇), and Xia Huang(黄霞). Chin. Phys. B, 2022, 31(7): 070505.
[11]	Design optimization of broadband extreme ultraviolet polarizer in high-dimensional objective space Shang-Qi Kuang(匡尚奇), Bo-Chao Li(李博超), Yi Wang(王依), Xue-Peng Gong(龚学鹏), and Jing-Quan Lin(林景全). Chin. Phys. B, 2022, 31(7): 077802.
[12]	The dynamics of a memristor-based Rulkov neuron with fractional-order difference Yan-Mei Lu(卢艳梅), Chun-Hua Wang(王春华), Quan-Li Deng(邓全利), and Cong Xu(徐聪). Chin. Phys. B, 2022, 31(6): 060502.
[13]	A mathematical analysis: From memristor to fracmemristor Wu-Yang Zhu(朱伍洋), Yi-Fei Pu(蒲亦非), Bo Liu(刘博), Bo Yu(余波), and Ji-Liu Zhou(周激流). Chin. Phys. B, 2022, 31(6): 060204.
[14]	Memristor-based multi-synaptic spiking neuron circuit for spiking neural network Wenwu Jiang(蒋文武), Jie Li(李杰), Hongbo Liu(刘洪波), Xicong Qian(钱曦聪), Yuan Ge(葛源), Lidan Wang(王丽丹), and Shukai Duan(段书凯). Chin. Phys. B, 2022, 31(4): 040702.
[15]	Complex dynamic behaviors in hyperbolic-type memristor-based cellular neural network Ai-Xue Qi(齐爱学), Bin-Da Zhu(朱斌达), and Guang-Yi Wang(王光义). Chin. Phys. B, 2022, 31(2): 020502.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Memristor's characteristics: From non-ideal to ideal

Cite this article:

Online attention