Prediction of Device Characteristics of Feedback Field-Effect Transistors Using TCAD-Augmented Machine Learning

Sola Woo; Juhee Jeon; Sangsig Kim

doi:10.3390/mi14030504

Prediction of Device Characteristics of Feedback Field-Effect Transistors Using TCAD-Augmented Machine Learning

Sola Woo, Juhee Jeon, Sangsig Kim

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

In this study, the device characteristics of silicon nanowire feedback field-effect transistors were predicted using technology computer-aided design (TCAD)-augmented machine learning (TCAD-ML). The full current–voltage (I-V) curves in forward and reverse voltage sweeps were predicted well, with high R-squared values of 0.9938 and 0.9953, respectively, by using random forest regression. Moreover, the TCAD-ML model provided high prediction accuracy not only for the full I-V curves but also for the important device features, such as the latch-up and latch-down voltages, saturation drain current, and memory window. Therefore, this study demonstrated that the TCAD-ML model can substantially reduce the computational time for device development compared with conventional simulation methods.

Original language	English
Article number	504
Journal	Micromachines
Volume	14
Issue number	3
DOIs	https://doi.org/10.3390/mi14030504
Publication status	Published - 2023 Mar

Bibliographical note

Funding Information:
This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2020R1A2C3004538, 2022M3I7A3046571) and the Brain Korea 21 Plus Project in 2023.

Publisher Copyright:
© 2023 by the authors.

Keywords

TCAD-augmented machine learning
feedback field-effect transistors
machine learning
random forest regression
technology computer-aided design (TCAD)

ASJC Scopus subject areas

Control and Systems Engineering
Mechanical Engineering
Electrical and Electronic Engineering

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10.3390/mi14030504

Cite this

@article{44a1834614e2439e86fedf882b414e7e,

title = "Prediction of Device Characteristics of Feedback Field-Effect Transistors Using TCAD-Augmented Machine Learning",

abstract = "In this study, the device characteristics of silicon nanowire feedback field-effect transistors were predicted using technology computer-aided design (TCAD)-augmented machine learning (TCAD-ML). The full current–voltage (I-V) curves in forward and reverse voltage sweeps were predicted well, with high R-squared values of 0.9938 and 0.9953, respectively, by using random forest regression. Moreover, the TCAD-ML model provided high prediction accuracy not only for the full I-V curves but also for the important device features, such as the latch-up and latch-down voltages, saturation drain current, and memory window. Therefore, this study demonstrated that the TCAD-ML model can substantially reduce the computational time for device development compared with conventional simulation methods.",

keywords = "TCAD-augmented machine learning, feedback field-effect transistors, machine learning, random forest regression, technology computer-aided design (TCAD)",

author = "Sola Woo and Juhee Jeon and Sangsig Kim",

note = "Funding Information: This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2020R1A2C3004538, 2022M3I7A3046571) and the Brain Korea 21 Plus Project in 2023. Publisher Copyright: {\textcopyright} 2023 by the authors.",

year = "2023",

month = mar,

doi = "10.3390/mi14030504",

language = "English",

volume = "14",

journal = "Micromachines",

issn = "2072-666X",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

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T1 - Prediction of Device Characteristics of Feedback Field-Effect Transistors Using TCAD-Augmented Machine Learning

AU - Woo, Sola

AU - Jeon, Juhee

AU - Kim, Sangsig

N1 - Funding Information: This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2020R1A2C3004538, 2022M3I7A3046571) and the Brain Korea 21 Plus Project in 2023. Publisher Copyright: © 2023 by the authors.

PY - 2023/3

Y1 - 2023/3

N2 - In this study, the device characteristics of silicon nanowire feedback field-effect transistors were predicted using technology computer-aided design (TCAD)-augmented machine learning (TCAD-ML). The full current–voltage (I-V) curves in forward and reverse voltage sweeps were predicted well, with high R-squared values of 0.9938 and 0.9953, respectively, by using random forest regression. Moreover, the TCAD-ML model provided high prediction accuracy not only for the full I-V curves but also for the important device features, such as the latch-up and latch-down voltages, saturation drain current, and memory window. Therefore, this study demonstrated that the TCAD-ML model can substantially reduce the computational time for device development compared with conventional simulation methods.

AB - In this study, the device characteristics of silicon nanowire feedback field-effect transistors were predicted using technology computer-aided design (TCAD)-augmented machine learning (TCAD-ML). The full current–voltage (I-V) curves in forward and reverse voltage sweeps were predicted well, with high R-squared values of 0.9938 and 0.9953, respectively, by using random forest regression. Moreover, the TCAD-ML model provided high prediction accuracy not only for the full I-V curves but also for the important device features, such as the latch-up and latch-down voltages, saturation drain current, and memory window. Therefore, this study demonstrated that the TCAD-ML model can substantially reduce the computational time for device development compared with conventional simulation methods.

KW - TCAD-augmented machine learning

KW - feedback field-effect transistors

KW - machine learning

KW - random forest regression

KW - technology computer-aided design (TCAD)

UR - http://www.scopus.com/inward/record.url?scp=85152214222&partnerID=8YFLogxK

U2 - 10.3390/mi14030504

DO - 10.3390/mi14030504

M3 - Article

AN - SCOPUS:85152214222

SN - 2072-666X

VL - 14

JO - Micromachines

JF - Micromachines

IS - 3

M1 - 504

ER -

Prediction of Device Characteristics of Feedback Field-Effect Transistors Using TCAD-Augmented Machine Learning

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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