Experimental Study of Memristors for use in Neuromorphic Computing

被引:0
|
作者
Zaman, Ayesha [1 ]
Shin, Eunsung [1 ]
Yakopcic, Chris [1 ]
Taha, Tarek M. [1 ]
Subramanyam, Guru [1 ]
机构
[1] Univ Dayton, Dept Elect & Comp Engn, Dayton, OH 45469 USA
关键词
memristor; neuromorphic; hysteresis; multi-level resistive switching; DEVICE; ARCHITECTURE; MECHANISM;
D O I
暂无
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Memristor devices have the potential to drive a new class of specialized low power embedded hardware. The unique characteristics of these non-volatile and nanoscale devices allow them to perform parallel analog computing with extreme efficiency. To help facilitate the design of such systems, this paper describes the fabrication and characterization process used to develop memristors that are strong candidates for use in neuromorphic systems. In this work two different types of memristor devices, those with a GeTe switching layer, and those with a VO2 switching layer, are characterized and analyzed. These results are used to determine device suitability for use in neuromorphic computing applications through the properties of symmetry, reliability, stability, and programmability. In short, repeatable multi-level resistive switching has been investigated and the results have been summarized.
引用
收藏
页码:370 / 374
页数:5
相关论文
共 50 条
  • [41] Optically Tunable Electrical Oscillations in Oxide-Based Memristors for Neuromorphic Computing
    Nath, Shimul Kanti
    Das, Sujan Kumar
    Nandi, Sanjoy Kumar
    Xi, Chen
    Marquez, Camilo Verbel
    Rua, Armando
    Uenuma, Mutsunori
    Wang, Zhongrui
    Zhang, Songqing
    Zhu, Rui-Jie
    Eshraghian, Jason
    Sun, Xiao
    Lu, Teng
    Bian, Yue
    Syed, Nitu
    Pan, Wenwu
    Wang, Han
    Lei, Wen
    Fu, Lan
    Faraone, Lorenzo
    Liu, Yun
    Elliman, Robert G.
    ADVANCED MATERIALS, 2024, 36 (25)
  • [42] Self-Doping Memristors with Equivalently Synaptic Ion Dynamics for Neuromorphic Computing
    Wang, Yaoyuan
    Zhang, Ziyang
    Xu, Mingkun
    Yang, Yifei
    Ma, Mingyuan
    Li, Huanglong
    Pei, Jing
    Shi, Luping
    ACS APPLIED MATERIALS & INTERFACES, 2019, 11 (27) : 24230 - 24240
  • [43] Molybdenum Disulfide Memristors for Next Generation Memory and Neuromorphic Computing: Progress and Prospects
    Wells, R. A.
    Robertson, A. W.
    ADVANCED ELECTRONIC MATERIALS, 2024, 10 (10):
  • [44] Coexistence of unipolar and bipolar resistive switching in optical synaptic memristors and neuromorphic computing
    Cui, Dongsheng
    Pei, Mengjiao
    Lin, Zhenhua
    Wang, Yifei
    Zhang, Hong
    Gao, Xiangxiang
    Yuan, Haidong
    Li, Yun
    Zhang, Jincheng
    Hao, Yue
    Chang, Jingjing
    CHIP, 2025, 4 (01):
  • [45] Interface engineering of amorphous gallium oxide crossbar array memristors for neuromorphic computing
    Masaoka, Naoki
    Hayashi, Yusuke
    Tohei, Tetsuya
    Sakai, Akira
    JAPANESE JOURNAL OF APPLIED PHYSICS, 2023, 62 (SC)
  • [46] Introduction to memristors and neuromorphic systems
    Chen, Xiaodong
    Hwang, Cheol Seong
    van de Burgt, Yoeri
    Santoro, Francesca
    MATERIALS HORIZONS, 2024, 11 (15) : 3462 - 3464
  • [47] 3D-structured mesoporous silica memristors for neuromorphic switching and reservoir computing
    Jaafar, Ayoub H.
    Shao, Li
    Dai, Peng
    Zhang, Tongjun
    Han, Yisong
    Beanland, Richard
    Kemp, Neil T.
    Bartlett, Philip N.
    Hector, Andrew L.
    Huang, Ruomeng
    NANOSCALE, 2022, 14 (46) : 17170 - 17181
  • [48] Efficient Neuromorphic Reservoir Computing Using Optoelectronic Memristors for Multivariate Time Series Classification
    Su, Jing
    Lu, Jiale
    Sun, Fan
    Zhou, Guangdong
    Duan, Shukai
    Hu, Xiaofang
    INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, 2023, 33 (06):
  • [49] CMOS back-end compatible memristors for in situ digital and neuromorphic computing applications
    He, Zhen-Yu
    Wang, Tian-Yu
    Meng, Jia-Lin
    Zhu, Hao
    Ji, Li
    Sun, Qing-Qing
    Chen, Lin
    Zhang, David Wei
    MATERIALS HORIZONS, 2021, 8 (12) : 3345 - 3355
  • [50] Fault-Tolerant Neuromorphic Computing With Memristors Using Functional ATPG for Efficient Recalibration
    Ahmed, Soyed Tuhin
    Tahoori, Mehdi B.
    IEEE DESIGN & TEST, 2023, 40 (04) : 42 - 50