Machine learning approach to predicting tunnel field-effect transistors

Chandni Akbar, Narasimhulu Thoti, Yi-Ming Li*

*Corresponding author for this work

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

4 Scopus citations

Abstract

We for the first time investigate the possibility to replace the device simulation for tunnel field-effect transistors (TFETs) with a machine learning (ML) algorithm. By incorporating the experimentally validated device simulation, a keyML technique named random forest regression (RFR) model is advanced and applied to predict characteristics of TFETs. The results of this work may benefit the design and fabrication of TFETs based on the well-trained RFR model. Very fast and accurate drain current (ID) prediction in terms of the engineering acceptable root-mean-square (RMSE) error inaugurates TFET technology with ML with a potential application to significantly reduce the computational cost.

Original languageEnglish
Title of host publicationVLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665419345
DOIs
StatePublished - 19 Apr 2021
Event2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021 - Hsinchu, Taiwan
Duration: 19 Apr 202122 Apr 2021

Publication series

NameVLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings

Conference

Conference2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021
Country/TerritoryTaiwan
CityHsinchu
Period19/04/2122/04/21

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