An organic terpyridyl-iron polymer based memristor for synaptic plasticity and learning behavior simulation

被引:49
|
作者
Yang, Xi [1 ,3 ,4 ]
Wang, Cheng [2 ]
Shang, Jie [3 ,4 ]
Zhang, Chaochao [3 ,4 ]
Tan, Hongwei [3 ,4 ]
Yi, Xiaohui [3 ,4 ]
Pan, Liang [3 ,4 ]
Zhang, Wenbin [3 ,4 ]
Fan, Fei [2 ,4 ]
Liu, Yaqing [1 ]
Chen, Yu [2 ]
Liu, Gang [3 ,4 ]
Li, Run-Wei [3 ,4 ]
机构
[1] North Univ China, Sch Mat Sci & Engn, Taiyuan 030051, Shanxi, Peoples R China
[2] E China Univ Sci & Technol, Inst Appl Chem, Shanghai 200237, Peoples R China
[3] Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Key Lab Magnet Mat & Devices, Ningbo 315201, Zhejiang, Peoples R China
[4] Chinese Acad Sci, Ningbo Inst Mat Technol & Engn, Zhejiang Prov Key Lab Magnet Mat & Applicat Techn, Ningbo 315201, Zhejiang, Peoples R China
来源
RSC ADVANCES | 2016年 / 6卷 / 30期
基金
中国国家自然科学基金;
关键词
RESISTIVE SWITCHING MEMORY; SUPRAMOLECULAR POLYMERS; TERM PLASTICITY; ANALOG VLSI; DEVICES; SYSTEMS; NEURON; STATE; SPECTROSCOPY; EXPERIENCE;
D O I
10.1039/c6ra02915a
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Memristors have been extensively studied for nonvolatile memory storage, neuromorphic computing, and logic applications. Particularly, synapse emulation is viewed as a key step to realizing neuromorphic computing, because the biological synapse is the basic unit for learning and memory. In this study, a memristor with the simple structure of Ta/viologen diperchlorate [EV(ClO4)(2)]/terpyridyl-iron polymer (TPy-Fe)/ITO is fabricated to simulate the functions of the synapse. Essential synaptic plasticity and learning behaviours are emulated by using this memristor, such as spike-timing-dependent plasticity and spike-rate-dependent plasticity. It is demonstrated that the redox between a terpyridyl-iron polymer and viologen species leads to our memristor behavior. Furthermore, the learning behavior depending on different amplitudes of voltage pulses is investigated as well. These demonstrations help pave the way for building bioinspired neuromorphic systems based on memristors.
引用
收藏
页码:25179 / 25184
页数:6
相关论文
共 50 条
  • [1] Novel Porphyrin-Containing Polymer Based Memristor for Synaptic Plasticity Simulation
    Bian, Linyi
    Xie, Meng
    Chong, Hao
    Zhang, Zhewei
    Liu, Guangyi
    Han, Qiushuo
    Ge, Jiaoyang
    Liu, Zheng
    Lei, Yang
    Zhang, Guangwei
    Xie, Linghai
    CHINESE JOURNAL OF CHEMISTRY, 2022, 40 (20) : 2451 - 2456
  • [2] Memristor based on α-In2Se3 for emulating biological synaptic plasticity and learning behavior
    Zhao, Ying
    Pei, Yifei
    Zhang, Zichang
    Li, Xiaoyu
    Wang, Jingjuan
    Yan, Lei
    He, Hui
    Zhou, Zhenyu
    Zhao, Jianhui
    Chen, Jingsheng
    Yan, Xiaobing
    SCIENCE CHINA-MATERIALS, 2022, 65 (06) : 1631 - 1638
  • [3] Solution-Processed Organic Memristor Matrix With Behavior of Clustered Synaptic Plasticity
    Huang, Hai-Tian
    Luo, Jie
    Wu, Jia-Ling
    Han, Xue-Er
    Zhang, Zhong-Da
    Cai, Jia-Wei
    Gao, Xu
    Xu, Jian-Long
    Zhong, Ya-Nan
    Dong, Bin
    Morozova, Sofia M.
    Wang, Sui-Dong
    IEEE ELECTRON DEVICE LETTERS, 2023, 44 (10) : 1724 - 1727
  • [4] Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications
    Zeng, Jianmin
    Chen, Xinhui
    Liu, Shuzhi
    Chen, Qilai
    Liu, Gang
    NANOMATERIALS, 2023, 13 (05)
  • [5] Memristor-based synaptic plasticity and unsupervised learning of spiking neural networks
    Hajiabadi, Zohreh
    Shalchian, Majid
    JOURNAL OF COMPUTATIONAL ELECTRONICS, 2021, 20 (04) : 1625 - 1636
  • [6] Memristor-based synaptic plasticity and unsupervised learning of spiking neural networks
    Zohreh Hajiabadi
    Majid Shalchian
    Journal of Computational Electronics, 2021, 20 : 1625 - 1636
  • [7] Neuromorphic memristor based on amorphous InAlZnO film for synaptic behavior simulation
    Xu, Yimeng
    Han, Xu
    Xu, Weidong
    Ye, Caiyang
    Dai, Ziyi
    Feng, Xianjin
    Qian, Kai
    APPLIED PHYSICS LETTERS, 2023, 123 (25)
  • [8] Synaptic plasticity features and neuromorphic system simulation in AlN-based memristor devices
    Kwon, Osung
    Lee, Yewon
    Kang, Myounggon
    Kim, Sungjun
    JOURNAL OF ALLOYS AND COMPOUNDS, 2022, 911
  • [9] Synaptic learning behavior based on a Ag/PEDOT: PSS/Ta memristor
    Luo, Wenqiang
    Wu, Xian
    Yuan, Fang-Yuan
    Wu, Huaqiang
    Pan, Liyang
    Deng, Ning
    2016 5TH INTERNATIONAL SYMPOSIUM ON NEXT-GENERATION ELECTRONICS (ISNE), 2016,
  • [10] A ZTO-based memristor with tunable synaptic plasticity
    Chen, Jianbiao
    Jia, Shuangju
    Gao, Liye
    Xu, Jiangwen
    Yang, Chunyan
    Guo, Tongtong
    Zhang, Pu
    Chen, Jiangtao
    Wang, Jian
    Zhao, Yun
    Zhang, Xuqiang
    Li, Yan
    COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS, 2024, 689