Automatic Prediction of Metal–Oxide–Semiconductor Field‐Effect Transistor Threshold Voltage Using Machine Learning Algorithm. (20th December 2022)

• Record Type: Journal Article
• Authors: Choi, Seoyeon; Park, Dong Geun; Kim, Min Jung; Bang, Seain; Kim, Jungchun; Jin, Seunghee; Huh, Ki Seok; Kim, Donghyun; Mitard, Jerome; Han, Cheol E.; Lee, Jae Woo
• Is Part Of: Advanced intelligent systems. Volume 5:Number 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: decision tree -- k -nearest neighbors -- machine learning -- MOSFET -- threshold-voltage extraction; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202200302
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : A fast and precise threshold voltage ( V th ) extraction method is required for the process design of electronic systems using metal–oxide–semiconductor field‐effect transistors (MOSFETs) and its immediate on‐site analysis during fabrication. The selection of a suitable V th extraction method is a complicated task because it involves a trade‐off between accuracy and simplicity according to the device scheme. Herein, an automatic‐prediction method of the MOSFET V th using machine learning (ML) is proposed. The ML model is trained with V th, extracted using different methods (2nd derivative, constant current, and Y ‐function) and from various kinds of FETs (finFET, 2D FET, and metal–oxide thin‐film transistors). The concept of threshold ratio ( R th ) for universal V th prediction, which considers the normalized V th within certain V G ranges, is suggested. The precision and accuracy of ML models are statistically verified by calculating the root mean square error (RMSE), mean absolute error, and mean coefficients of determination ( R 2 ) values. The universal ML model ( k ‐nearest neighbor (kNN)) achieves 1.35% of RMSE and 0.98 of R 2 for the best score. The ML model eliminates the ambiguity in V th extraction and provides objective V th prediction for most FET schemes used in the semiconductor industry and research field. Abstract : An automatic prediction method of threshold voltage ( V th ) using machine learning (ML) is investigated for various field‐effect transistors (FETs). Using threshold ratio, ML is generally applicable regardless of device schemes. ML model shows great performance (1.35% of RMSE and 0.98 of R 2 ). The automatic method enables us to predict the object V th of any FET.