Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence

Yun Dei; Ya Shu Yang; Yiming Li

doi:10.23919/SISPAD57422.2023.10319499

Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence

Yun Dei^*
, Ya Shu Yang
, Yiming Li

^*Corresponding author for this work

Institute of Communications Engineering

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

1 Scopus citations

Abstract

We propose a compact model that utilizes physical-based artificial neural networks (ANNs) to model the effect of temperature on n- and p-type gate-all-around nanosheet FETs. Our compact model comprises two independent ANNs, where the first ANN is designed to output parameters related to temperature and the second ANN is utilized for the device physical parameters. All outputs of ANNs are integrated into a physical equation of drain current to form the entire compact model. Compared with the BSIM-CMG model in circuit simulations, our results are highly consistent in transfer characteristics and timing dynamics.

Original language	English
Title of host publication	2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	285-288
Number of pages	4
ISBN (Electronic)	9784863488038
DOIs	https://doi.org/10.23919/SISPAD57422.2023.10319499
State	Published - 2023
Event	2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023 - Kobe, Japan Duration: 27 Sep 2023 → 29 Sep 2023

Publication series

Name	International Conference on Simulation of Semiconductor Processes and Devices, SISPAD

Conference

Conference	2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023
Country/Territory	Japan
City	Kobe
Period	27/09/23 → 29/09/23

Keywords

Artificial neural networks
GAA NS MOSFETs
physical-based compact modelling methodology
temperature dependence

Access to Document

10.23919/SISPAD57422.2023.10319499

Cite this

Dei, Y., Yang, Y. S., & Li, Y. (2023). Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence. In 2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023 (pp. 285-288). (International Conference on Simulation of Semiconductor Processes and Devices, SISPAD). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/SISPAD57422.2023.10319499

Dei, Yun ; Yang, Ya Shu ; Li, Yiming. / Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence. 2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 285-288 (International Conference on Simulation of Semiconductor Processes and Devices, SISPAD).

@inproceedings{41f7368a1a234fec969bace75735d3a8,

title = "Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence",

abstract = "We propose a compact model that utilizes physical-based artificial neural networks (ANNs) to model the effect of temperature on n- and p-type gate-all-around nanosheet FETs. Our compact model comprises two independent ANNs, where the first ANN is designed to output parameters related to temperature and the second ANN is utilized for the device physical parameters. All outputs of ANNs are integrated into a physical equation of drain current to form the entire compact model. Compared with the BSIM-CMG model in circuit simulations, our results are highly consistent in transfer characteristics and timing dynamics.",

keywords = "Artificial neural networks, GAA NS MOSFETs, physical-based compact modelling methodology, temperature dependence",

author = "Yun Dei and Yang, \{Ya Shu\} and Yiming Li",

note = "Publisher Copyright: {\textcopyright} 2023 The Japan Society of Applied Physics.; 2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023 ; Conference date: 27-09-2023 Through 29-09-2023",

year = "2023",

doi = "10.23919/SISPAD57422.2023.10319499",

language = "English",

series = "International Conference on Simulation of Semiconductor Processes and Devices, SISPAD",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "285--288",

booktitle = "2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023",

address = "美國",

}

Dei, Y, Yang, YS & Li, Y 2023, Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence. in 2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023. International Conference on Simulation of Semiconductor Processes and Devices, SISPAD, Institute of Electrical and Electronics Engineers Inc., pp. 285-288, 2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023, Kobe, Japan, 27/09/23. https://doi.org/10.23919/SISPAD57422.2023.10319499

Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence. / Dei, Yun; Yang, Ya Shu; Li, Yiming.
2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 285-288 (International Conference on Simulation of Semiconductor Processes and Devices, SISPAD).

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

TY - GEN

T1 - Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence

AU - Dei, Yun

AU - Yang, Ya Shu

AU - Li, Yiming

PY - 2023

Y1 - 2023

N2 - We propose a compact model that utilizes physical-based artificial neural networks (ANNs) to model the effect of temperature on n- and p-type gate-all-around nanosheet FETs. Our compact model comprises two independent ANNs, where the first ANN is designed to output parameters related to temperature and the second ANN is utilized for the device physical parameters. All outputs of ANNs are integrated into a physical equation of drain current to form the entire compact model. Compared with the BSIM-CMG model in circuit simulations, our results are highly consistent in transfer characteristics and timing dynamics.

AB - We propose a compact model that utilizes physical-based artificial neural networks (ANNs) to model the effect of temperature on n- and p-type gate-all-around nanosheet FETs. Our compact model comprises two independent ANNs, where the first ANN is designed to output parameters related to temperature and the second ANN is utilized for the device physical parameters. All outputs of ANNs are integrated into a physical equation of drain current to form the entire compact model. Compared with the BSIM-CMG model in circuit simulations, our results are highly consistent in transfer characteristics and timing dynamics.

KW - Artificial neural networks

KW - GAA NS MOSFETs

KW - physical-based compact modelling methodology

KW - temperature dependence

UR - https://www.scopus.com/pages/publications/85179137422

U2 - 10.23919/SISPAD57422.2023.10319499

DO - 10.23919/SISPAD57422.2023.10319499

M3 - Conference contribution

AN - SCOPUS:85179137422

T3 - International Conference on Simulation of Semiconductor Processes and Devices, SISPAD

SP - 285

EP - 288

BT - 2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023

Y2 - 27 September 2023 through 29 September 2023

ER -

Dei Y, Yang YS, Li Y. Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence. In 2023 International Conference on Simulation of Semiconductor Processes and Devices, SISPAD 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 285-288. (International Conference on Simulation of Semiconductor Processes and Devices, SISPAD). doi: 10.23919/SISPAD57422.2023.10319499

Compact Modeling of N- and P-Type GAA NS FETs Using Physical-Based Artificial Neural Networks with Temperature Dependence

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Keywords

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