반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

TCAD-머신러닝 기반 나노시트 FETs 컴팩트 모델링

Compact Modeling for Nanosheet FET Based on TCAD-Machine Learning

  • 송준혁 (상명대학교 시스템반도체 공학과) ;
  • 이운복 (상명대학교 시스템반도체 공학과) ;
  • 이종환 (상명대학교 시스템반도체 공학과)
  • Junhyeok Song (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Wonbok Lee (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Jonghwan Lee (Department of System Semiconductor Engineering, Sangmyung University)
  • 투고 : 2023.12.01
  • 심사 : 2023.12.14
  • 발행 : 2023.12.31
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초록

The continuous shrinking of transistors in integrated circuits leads to difficulties in improving performance, resulting in the emerging transistors such as nanosheet field-effect transistors. In this paper, we propose a TCAD-machine learning framework of nanosheet FETs to model the current-voltage characteristics. Sentaurus TCAD simulations of nanosheet FETs are performed to obtain a large amount of device data. A machine learning model of I-V characteristics is trained using the multi-layer perceptron from these TCAD data. The weights and biases obtained from multi-layer perceptron are implemented in a PSPICE netlist to verify the accuracy of I-V and the DC transfer characteristics of a CMOS inverter. It is found that the proposed machine learning model is applicable to the prediction of nanosheet field-effect transistors device and circuit performance.

키워드

과제정보

This research was supported in parts by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2022R1I1A3064285).

참고문헌

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