Predictive Modeling of Ferroelectric Tunnel Junctions for Memory and Analog Weight Cell Applications

Yi Xiao, Shan Deng, Zijian Zhao, Vijaykrishnan Narayanan, Kai Ni

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

5 Scopus citations

Abstract

Most ferroelectric tunnel junction (FTJ) models lack the predictive capability due to their incomplete capture of the dynamic polarization switching in a multi-domain ferroelectric thin film and the multi-band tunneling transport, limiting their usage in write-Aware design optimizations. In this work, we demonstrate: i) a predictive metal-ferroelectric-insulator-semiconductor (MFIS) FTJ model by incorporating a polarization-switching module and a multi-band tunneling module which is calibrated with device data on both n-Type and p-Type substrates; ii) asymmetric polarization states induced by positive/negative write pulses due to the absence of minority carriers in a two-Terminal MFIS FTJ, which is typically neglected in previous FTJ models; iii) write-Aware design space exploration for memory and analog synapse applications, which is beyond the capability of existing FTJ models.

Original languageEnglish (US)
Title of host publication2021 IEEE International Electron Devices Meeting, IEDM 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages15.5.1-15.5.4
ISBN (Electronic)9781665425728
DOIs
StatePublished - 2021
Event2021 IEEE International Electron Devices Meeting, IEDM 2021 - San Francisco, United States
Duration: Dec 11 2021Dec 16 2021

Publication series

NameTechnical Digest - International Electron Devices Meeting, IEDM
Volume2021-December
ISSN (Print)0163-1918

Conference

Conference2021 IEEE International Electron Devices Meeting, IEDM 2021
Country/TerritoryUnited States
CitySan Francisco
Period12/11/2112/16/21

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering
  • Materials Chemistry

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