A methodology to improve linearity of analog RRAM for neuromorphic computing

Wei Wu, Huaqiang Wu, Bin Gao, Peng Yao, Xiang Zhang, Xiaochen Peng, Shimeng Yu, He Qian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

52 Scopus citations

Abstract

The conductance tuning linearity is an important parameter of analog RRAM for neuromorphic computing. This work presents a novel methodology to improve the conductance tuning linearity of the filamentary RRAM. An electro-thermal modulation layer is designed and introduced to control the distribution of electric field and temperature in the filament region. For the first time, a HfOx based RRAM is demonstrated with linear analog SET, linear analog RESET, 50ns speed, 10× analog tuning window, 100kω on-state resistance, and high temperature retention for multilevel states. The excellent performances of the analog RRAM devices enable high accuracy online learning in a neural network.

Original languageEnglish (US)
Title of host publication2018 IEEE Symposium on VLSI Technology, VLSI Technology 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages103-104
Number of pages2
Volume2018-June
ISBN (Electronic)9781538642160
DOIs
StatePublished - Oct 25 2018
Event38th IEEE Symposium on VLSI Technology, VLSI Technology 2018 - Honolulu, United States
Duration: Jun 18 2018Jun 22 2018

Other

Other38th IEEE Symposium on VLSI Technology, VLSI Technology 2018
CountryUnited States
CityHonolulu
Period6/18/186/22/18

Keywords

  • Analog RRAM
  • Online learning
  • Synapse

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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