Computational Associative Memory Powered by Ferroelectric Memory

Kai Ni, Yi Xiao, Shan Deng, Vijaykrishnan Narayanan

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

1 Scopus citations

Abstract

Discovery of ferroelectricity in thin doped Hf02 has revived the interest in ferroelectric memories because its excellent CMOS-compatibility, great scalability, and superior energy efficiency. Various types of ferroelectric memories are under consideration, including capacitor based ferroelectric random access memory (FeRAM) and transistor based ferroelectric field effect transistor (FeFET). Significant progress has been made by integrating them at advanced technology nodes. These exciting developments have made ferroelectric memories prime candidates as embedded nonvolatile memory and enable their application in compute-in-memory (CiM) accelerators. One important class of CiM kernel is content addressable memory (CAM), where the memory is addressed through its content. In this work, we overview different designs of FeFET based CAM and propose FeRAM based CAM.

Original languageEnglish (US)
Title of host publication2023 Device Research Conference, DRC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350323108
DOIs
StatePublished - 2023
Event2023 Device Research Conference, DRC 2023 - Santa Barbara, United States
Duration: Jun 25 2023Jun 28 2023

Publication series

NameDevice Research Conference - Conference Digest, DRC
Volume2023-June
ISSN (Print)1548-3770

Conference

Conference2023 Device Research Conference, DRC 2023
Country/TerritoryUnited States
CitySanta Barbara
Period6/25/236/28/23

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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