A Dynamical Compact Model of Diffusive and Drift Memristors for Neuromorphic Computing

Abstract

Different from nonvolatile memory applications, neuromorphic computing applications utilize not only the static conductance states but also the switching dynamics for computing, which calls for compact dynamical models of memristive devices. In this work, a generalized model to simulate diffusive and drift memristors with the same set of equations is presented, which have been used to reproduce experimental results faithfully. The diffusive memristor is chosen as the basis for the generalized model because it possesses complex dynamical properties that are difficult to model efficiently. A data set from statistical measurements on SiO2:Ag diffusive memristors is collected to verify the validity of the general model. As an application example, spike‐timing‐dependent plasticity is demonstrated with an artificial synapse consisting of a diffusive memristor and a drift memristor, both modeled with this comprehensive compact model.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 13, 2021
Source ID
10.1002/aelm.202100696

Entities

People

  • Hao Jiang
  • Jianhua Joshua Yang
  • Mingyi Rao
  • Peng Lin
  • Qiangfei Xia
  • R. Stanley Williams
  • Rivu Midya
  • Shiva Asapu
  • Wenhao Song
  • Ye Zhuo
  • Zhongrui Wang

Organizations

  • Air Force Office of Scientific Research
  • Air Force Research Laboratory
  • National Science Foundation
  • University of Massachusetts
  • University of Southern California

Tags

Readers

  • Computational Modeling and Simulation
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Integrated Circuit Design and Technology.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms