Alloying conducting channels for reliable neuromorphic computing.

Journal: Nature nanotechnology
Published Date: Jun 8, 2020

Abstract

A memristor has been proposed as an artificial synapse for emerging neuromorphic computing applications. To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform. An electrochemical metallization (ECM) memory, typically based on silicon (Si), has demonstrated a good analogue switching capability owing to the high mobility of metal ions in the Si switching medium. However, the large stochasticity of the ion movement results in switching variability. Here we demonstrate a Si memristor with alloyed conduction channels that shows a stable and controllable device operation, which enables the large-scale implementation of crossbar arrays. The conduction channel is formed by conventional silver (Ag) as a primary mobile metal alloyed with silicidable copper (Cu) that stabilizes switching. In an optimal alloying ratio, Cu effectively regulates the Ag movement, which contributes to a substantial improvement in the spatial/temporal switching uniformity, a stable data retention over a large conductance range and a substantially enhanced programmed symmetry in analogue conductance states. This alloyed memristor allows the fabrication of large-scale crossbar arrays that feature a high device yield and accurate analogue programming capability. Thus, our discovery of an alloyed memristor is a key step paving the way beyond von Neumann computing.

Authors

  • Hanwool Yeon
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Peng Lin
    Zhejiang Key Laboratory of Excited-State Energy Conversion and Energy Storage, Department of Chemistry, Zhejiang University, Hangzhou 310058, China.
  • Chanyeol Choi
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Scott H Tan
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Yongmo Park
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Doyoon Lee
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Jaeyong Lee
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Feng Xu
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Bin Gao
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: gaob1@tsinghua.edu.cn.
  • Huaqiang Wu
    Institue of Microelectronics, Tsinghua University, Beijing, 100084, China.
  • He Qian
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: qianh@tsinghua.edu.cn.
  • Yifan Nie
    Sino-French Engineer School, Beihang University, Beijing 100191, P. R. China.
  • Seyoung Kim
    IBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
  • Jeehwan Kim
    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. jeehwan@mit.edu.

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