BEOL Compatible Superlattice FerroFET-based High Precision Analog Weight Cell with Superior Linearity and Symmetry

Khandker Akif Aabrar*, Jorge Gomez, Sharadindu Gopal Kirtania, Matthew San Jose, Yandong Luo, Priyankka G. Ravikumar, Prasanna Venkatesan Ravindran, Huacheng Ye, Sanjukta Banerjee, Sourav Dutta, Asif Islam Khan, Shimeng Yu, Suman Datta

*Corresponding author for this work

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

24 Scopus citations

Abstract

Off-chip DRAM memory accesses limit the energy efficiency and training time of state-of-The-Art deep neural networks (DNN). Compute-in-memory (CIM) accelerators leveraging pseudo-crossbar arrays and on-chip weight storage have emerged as alternatives to GPUs for fast and efficient training. However, this comes at the cost of reduced training accuracy due to weight cell non-idealities such as: low bit precision, nonlinearity, asymmetry, low Gmax/Gmin ratio, and slow programming speed. Here, we engineer the ferroelectric domain structure in a carefully designed superlattice (SL) ferroelectric(FE)/dielectric(DE) stack, to experimentally demonstrate high precision FEFET analog weight cells with excellent linearity and symmetry during potentiation and depression. We demonstrate switching speed as low as 100 ns in the SL-based ferroelectric capacitor (FECAP), with no degradation in either retention or endurance. We integrate the SL FE/DE/FE with a back-end-of-line (BEOL) compatible Indium Tungsten Oxide transistors, to demonstrate 128 stable conductance states with improved linearity and symmetry. System-level analysis of SL-FEFET based CIM accelerators show an excellent 94.1% online learning accuracy without degrading any other performance parameter, with potential for monolithic 3D integration.

Original languageEnglish
Title of host publication2021 IEEE International Electron Devices Meeting, IEDM 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19.6.1-19.6.4
ISBN (Electronic)9781665425728
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Electron Devices Meeting, IEDM 2021 - San Francisco, United States
Duration: 11 Dec 202116 Dec 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
Period11/12/2116/12/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

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