@inproceedings{a5850257021f4970a132d6fd87162b67,
title = "Machine learning approach to predicting tunnel field-effect transistors",
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. ",
author = "Chandni Akbar and Narasimhulu Thoti and Yi-Ming Li",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021 ; Conference date: 19-04-2021 Through 22-04-2021",
year = "2021",
month = apr,
day = "19",
doi = "10.1109/VLSI-TSA51926.2021.9440136",
language = "English",
series = "VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings",
address = "United States",
}