Tunable Non-Volatile Analog Resistive Memory and Its Application in AI

Heba Abunahla, Yasmin Halawani, Baker Mohammad, Anas Alazzam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This paper presents a novel analog resistive random-access memory (RRAM), named NeuroMem, which consists of Au/GO/Au. The device's resistance can be tuned to any value within its ROFF to RON range with high precision. The analog characteristic of NeuroMem mimics the memorization behavior of the brain, which makes it great asset for artificial neural network applications. In this work, NeuroMem-based crossbars are fabricated to hold the synaptic weights needed to perform Iris classification. The weight values are mapped to conductance states within NeuroMem ROFF to RON range, and then written accurately on the actual devices. Unlike other RRAM-based hardware with limited conductance states, in this work no quantization is needed which enables efficient inmemory-computing without scarifying accuracy. Furthermore, Neuromem device has been demonstrated in crossbars on flexible polymer substrate using standard photolithography process, which facilitates producing low cost flexible electronics. This work opens up great insights towards realizing RRAM-based computing at the edge.

Original languageBritish English
Title of host publication2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages464-467
Number of pages4
ISBN (Electronic)9781538629161
DOIs
StatePublished - Aug 2020
Event63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Springfield, United States
Duration: 9 Aug 202012 Aug 2020

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2020-August
ISSN (Print)1548-3746

Conference

Conference63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020
Country/TerritoryUnited States
CitySpringfield
Period9/08/2012/08/20

Keywords

  • analog
  • classification
  • graphene oxide
  • neural network
  • RRAM

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