Automatic Prediction of Metal–Oxide–Semiconductor Field-Effect Transistor Threshold Voltage Using Machine Learning Algorithm

Abstract

A fast and precise threshold voltage (Vth) 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 Vth 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 Vth using machine learning (ML) is proposed. The ML model is trained with Vth, 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 (Rth) for universal Vth prediction, which considers the normalized Vth within certain VG 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 (R2) values. The universal ML model (k-nearest neighbor (kNN)) achieves 1.35% of RMSE and 0.98 of R2 for the best score. The ML model eliminates the ambiguity in Vth extraction and provides objective Vth prediction for most FET schemes used in the semiconductor industry and research field.