Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs

Rajat Butola, Yiming Li*, Sekhar Reddy Kola

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In this paper, for the first time, deep learning (DL) based artificial neural network (ANN) is applied to model the effects of various random variations: work function fluctuation, random dopant fluctuation, and interface trap fluctuation, on gate-all-around silicon nanosheet MOSFETs. The number of fluctuations for each source variation is used as input features and their effects on devices of interest are studied qualitatively and quantitatively. The key figures of merit (FoM) are also extracted accurately from the transfer characteristics, which shows the competency of the ANN model in the domain of device modeling.

原文English
主出版物標題2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665409230
DOIs
出版狀態Published - 2022
事件2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022 - Hsinchu, Taiwan
持續時間: 18 4月 202221 4月 2022

出版系列

名字2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

Conference

Conference2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022
國家/地區Taiwan
城市Hsinchu
期間18/04/2221/04/22

指紋

深入研究「Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs」主題。共同形成了獨特的指紋。

引用此