A Model-Based-Random-Forest Framework for Predicting <inline-formula> <tex-math notation="LaTeX">$V_{t}$ </tex-math> </inline-formula> Mean and Variance Based on Parallel <inline-formula> <tex-math notation="LaTeX">$I_{d}$ </tex-math> </inline-formula> Measurement

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

“…Random forest regression models are faster and more accurate than boosted regressions [ 21 ]; therefore, we selected this model for the prediction of device characteristics. In the TCAD-ML model, device characteristics were predicted using linear regression, neural network, and random forest regression algorithms [ 22 , 23 , 24 , 25 ]. However, linear regression has limitations for the prediction of nonlinear dependency despite its simple design [ 22 ].…”