Controllable high-performance memristors based on 2D FeGeTeoxide for biological synapse imitation.

Journal: Nanotechnology
PMID: 33930891

Abstract

Memristors are an important component of the next-generation artificial neural network, high computing systems, etc. In the past, two-dimensional materials based memristors have achieved a high performance and low power consumption, though one at the cost of the other. Furthermore, their performance can not be modulated frequently once their structures are fixed, which remains the bottleneck in the development. Herein, a series of forming free memristors are fabricated with the same Cu/FeGeTeoxide/FeGeTe/Al structure, yet the On/Off ratio and set voltage is modulated continuously by varying the oxidation time during fabrication. With an optimal oxidation time, a large On/Off ratio (1.58 × 10) and low set voltage (0.74 V) is achieved in a single device. The formation and rapture of Al conductive filaments are found to be responsible for the memristors, and the filaments density and the cross-section area increase with the increase of current compliance, which achieves a higher On/Off ratio. The memristor can imitate basic biological synaptic functions using voltage pulses, demonstrating the potential for low-power consuming neuromorphic computing applications.

Authors

  • Xiangyu Zeng
    Department of Plant Pathology, College of Agriculture, Guizhou University, Guiyang, Guizhou, PR China; Institute of Edible Mushroom, Guizhou University, Guiyang, PR China.
  • Shuyi Huang
    College of Information Science and Electronic Engineering, Hangzhou 310027, People's Republic of China.
  • Qikai Ye
    College of Information Science and Electronic Engineering, Hangzhou 310027, People's Republic of China.
  • Pandey Rajagopalan
    College of Information Science and Electronic Engineering, Hangzhou 310027, People's Republic of China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Haoze Kuang
    College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
  • Ge Ye
    Center for correlated matter and Department of Physics, Zhejiang University, Hangzhou 310027, People's Republic of China.
  • Chufan Chen
    Center for correlated matter and Department of Physics, Zhejiang University, Hangzhou 310027, People's Republic of China.
  • Menglu Li
    School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China.
  • Yulu Liu
    BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China. liuyulu@genomics.cn.
  • Lin Shi
    Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, China.
  • Yuzheng Guo
    School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.
  • Xin Lu
    CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
  • Wenhua Shi
    Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, People's Republic of China.
  • Jikui Luo
    College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
  • Xiaozhi Wang
    College of Information Science and Electronic Engineering, Hangzhou 310027, People's Republic of China.

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