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

Rajat Butola; Yiming Li; Sekhar Reddy Kola

doi:10.1109/VLSI-TSA54299.2022.9771019

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

電信工程研究所

研究成果: Conference contribution › 同行評審

5 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	https://doi.org/10.1109/VLSI-TSA54299.2022.9771019
出版狀態	Published - 2022
事件	2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022 - Hsinchu, 台灣持續時間: 18 4月 2022 → 21 4月 2022

出版系列

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

Conference

Conference	2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022
國家/地區	台灣
城市	Hsinchu
期間	18/04/22 → 21/04/22

存取文件

10.1109/VLSI-TSA54299.2022.9771019

其它文件與鏈接

Link to publication in Scopus

引用此

Butola, R., Li, Y., & Kola, S. R. (2022). Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs. 於 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022 (2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VLSI-TSA54299.2022.9771019

Butola, Rajat ; Li, Yiming ; Kola, Sekhar Reddy. / Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs. 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022).

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

abstract = "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.",

author = "Rajat Butola and Yiming Li and Kola, {Sekhar Reddy}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022 ; Conference date: 18-04-2022 Through 21-04-2022",

year = "2022",

doi = "10.1109/VLSI-TSA54299.2022.9771019",

language = "English",

series = "2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022",

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Butola, R, Li, Y & Kola, SR 2022, Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs. 於 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022. 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022, Institute of Electrical and Electronics Engineers Inc., 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022, Hsinchu, 台灣, 18/04/22. https://doi.org/10.1109/VLSI-TSA54299.2022.9771019

Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs. / Butola, Rajat; Li, Yiming; Kola, Sekhar Reddy.
2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022).

研究成果: Conference contribution › 同行評審

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T1 - Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs

AU - Butola, Rajat

AU - Li, Yiming

AU - Kola, Sekhar Reddy

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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U2 - 10.1109/VLSI-TSA54299.2022.9771019

DO - 10.1109/VLSI-TSA54299.2022.9771019

M3 - Conference contribution

AN - SCOPUS:85130459018

T3 - 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

Y2 - 18 April 2022 through 21 April 2022

ER -

Butola R, Li Y, Kola SR. Deep Learning Approach to Modeling and Exploring Random Sources of Gate-All-Around Silicon Nanosheet MOSFETs. 於 2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022). doi: 10.1109/VLSI-TSA54299.2022.9771019

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

摘要

出版系列

Conference

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其它文件與鏈接

指紋

引用此