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 ID-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.

Automated summary

The study introduces an algorithm using machine learning to predict device characteristic variations, which shows the same accuracy as traditional device simulation but with reduced computation costs, driving energy-efficient devices.