Machine Learning Aided Device Simulation of Work Function Fluctuation for Multichannel Gate-All-Around Silicon Nanosheet MOSFETs

A machine learning (ML) aided device simulation of work function fluctuation (WKF) for 3-D multichannel gate-all-around silicon nanosheet MOSFET is presented. To establish the ML model, the random forest regressor (RFR) is explored to predict the characteristic variation of the explored device. The proposed ML-RFR algorithm for predicting the
${I} _{D}$
–
${V} _{G}$
curve shows the same degree of accuracy as device simulation and it also estimates the minimum required samples for the converged ML-RFR model, i.e., 330 samples. By using the root mean squared error value, error rate, and
${R} ^{{2}}$
score as the evaluation tools, our ML-RFR model infers with an
${R} ^{{2}}$
score of 99% and an error rate of less than 1%. The main objective of this work is to explore the possibility of ML model that can replace the device simulation to reduce the computational cost and drive energy-efficient devices.