ABO multiferroic perovskite materials for memristive memory and neuromorphic computing.

Journal: Nanoscale horizons
Published Date:

Abstract

The unique electron spin, transfer, polarization and magnetoelectric coupling characteristics of ABO multiferroic perovskite materials make them promising candidates for application in multifunctional nanoelectronic devices. Reversible ferroelectric polarization, controllable defect concentration and domain wall movement originated from the ABO multiferroic perovskite materials promotes its memristive effect, which further highlights data storage, information processing and neuromorphic computing in diverse artificial intelligence applications. In particular, ion doping, electrode selection, and interface modulation have been demonstrated in ABO-based memristive devices for ultrahigh data storage, ultrafast information processing, and efficient neuromorphic computing. These approaches presented today including controlling the dopant in the active layer, altering the oxygen vacancy distribution, modulating the diffusion depth of ions, and constructing the interface-dependent band structure were believed to be efficient methods for obtaining unique resistive switching (RS) behavior for various applications. In this review, internal physical dynamics, preparation technologies, and modulation methods are systemically examined as well as the progress, challenges, and possible solutions are proposed for next generation emerging ABO-based memristive application in artificial intelligence.

Authors

  • Bai Sun
    Department of Mechanical and Mechatronics Engineering, Waterloo Institute for Nanotechnology, Centre for Advanced Materials Joining, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
  • Guangdong Zhou
    School of Artificial Intelligence, Southwest University, Chongqing 400715, China.
  • Linfeng Sun
    Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China.
  • Hongbin Zhao
    State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China.
  • Yuanzheng Chen
    School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials (Ministry of Education of China), Southwest Jiaotong University, Chengdu, Sichuan 610031, China.
  • Feng Yang
  • Yong Zhao
    a School of Mathematics and Information Science , Henan Polytechnic University , Jiaozuo 454000 , People's Republic of China.
  • Qunliang Song
    School of Artificial Intelligence and School of Materials and Energy, Southwest University, Chongqing 400715, China. qlsong@swu.edu.cn.