Machine learning approach to predicting tunnel field-effect transistors

Chandni Akbar; Narasimhulu Thoti; Yi-Ming Li

doi:10.1109/VLSI-TSA51926.2021.9440136

Machine learning approach to predicting tunnel field-effect transistors

Chandni Akbar, Narasimhulu Thoti, Yi-Ming Li^*

^*Corresponding author for this work

Institute of Communications Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 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 language	English
Title of host publication	VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665419345
DOIs	https://doi.org/10.1109/VLSI-TSA51926.2021.9440136
State	Published - 19 Apr 2021
Event	2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021 - Hsinchu, Taiwan Duration: 19 Apr 2021 → 22 Apr 2021

Publication series

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

Conference

Conference	2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021
Country/Territory	Taiwan
City	Hsinchu
Period	19/04/21 → 22/04/21

Access to Document

10.1109/VLSI-TSA51926.2021.9440136

Cite this

Akbar, C., Thoti, N., & Li, Y.-M. (2021). Machine learning approach to predicting tunnel field-effect transistors. In VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings Article 9440136 (VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VLSI-TSA51926.2021.9440136

Akbar, Chandni ; Thoti, Narasimhulu ; Li, Yi-Ming. / Machine learning approach to predicting tunnel field-effect transistors. VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings).

@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 = "美國",

}

Akbar, C, Thoti, N & Li, Y-M 2021, Machine learning approach to predicting tunnel field-effect transistors. in VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings., 9440136, VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021, Hsinchu, Taiwan, 19/04/21. https://doi.org/10.1109/VLSI-TSA51926.2021.9440136

Machine learning approach to predicting tunnel field-effect transistors. / Akbar, Chandni; Thoti, Narasimhulu; Li, Yi-Ming.
VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. 9440136 (VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine learning approach to predicting tunnel field-effect transistors

AU - Akbar, Chandni

AU - Thoti, Narasimhulu

AU - Li, Yi-Ming

PY - 2021/4/19

Y1 - 2021/4/19

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85108160609&partnerID=8YFLogxK

U2 - 10.1109/VLSI-TSA51926.2021.9440136

DO - 10.1109/VLSI-TSA51926.2021.9440136

M3 - Conference contribution

AN - SCOPUS:85108160609

T3 - VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2021

Y2 - 19 April 2021 through 22 April 2021

ER -

Akbar C, Thoti N, Li YM. Machine learning approach to predicting tunnel field-effect transistors. In VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. 9440136. (VLSI-TSA 2021 - 2021 International Symposium on VLSI Technology, Systems and Applications, Proceedings). doi: 10.1109/VLSI-TSA51926.2021.9440136

Machine learning approach to predicting tunnel field-effect transistors

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this