Computing-in-Memory with Ferroelectric Materials and Beyond

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

Abstract

Recent discovery of hafnium-based ferroelectric (FE) materials opens up numerous CMOS-compatible memory device opportunities: FE capacitors, FE-FETs, and FE tunnel junctions. These devices offer significant advantages in endurance, write speed, and power compared to today's flash. In this paper, we give a brief overview of FE materials/devices fundamentals, and its applicability towards computing-in-memory (CIM). We discuss CIM realized using FE-FinFET arrays with various possible configurations. Alternatively, FE capacitors in CMOS backend may be used in non-volatile SRAM cells to store contents of CIM-SRAM before powering off. We also present CIMulator, a simulation platform to account for device, circuit, and neural network aspects CIM macros with deep machine learning applications.

Original languageEnglish
Title of host publication2023 International VLSI Symposium on Technology, Systems and Applications, VLSI-TSA/VLSI-DAT 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350334166
DOIs
Publication statusPublished - 2023
Event2023 International VLSI Symposium on Technology, Systems and Applications, VLSI-TSA/VLSI-DAT 2023 - Hsinchu, Taiwan
Duration: 2023 Apr 172023 Apr 20

Publication series

Name2023 International VLSI Symposium on Technology, Systems and Applications, VLSI-TSA/VLSI-DAT 2023 - Proceedings

Conference

Conference2023 International VLSI Symposium on Technology, Systems and Applications, VLSI-TSA/VLSI-DAT 2023
Country/TerritoryTaiwan
CityHsinchu
Period23-04-1723-04-20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Electrical and Electronic Engineering

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