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

Sola Woo, Juhee Jeon, Sangsig Kim

Research output: Contribution to journalArticlepeer-review

3 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 languageEnglish
Article number504
JournalMicromachines
Volume14
Issue number3
DOIs
Publication statusPublished - 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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