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

• Published in: IEEE transactions on electron devices Vol. 68; no. 11; pp. 5490 - 5497
• Main Authors: Akbar, Chandni, Li, Yiming, Sung, Wen Li
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Data models; Errors; Gallium arsenide; Gate-all-around (GAA); Machine learning; machine learning (ML); MOSFET; MOSFETs; nanosheet (NS); Nanosheets; Predictive models; random forest regressor (RFR); Semiconductor device modeling; Silicon; Simulation; Solid modeling; work function fluctuation (WKF); Work functions
• ISSN: 0018-9383; 1557-9646
• DOI: 10.1109/TED.2021.3084910

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 <inline-formula> <tex-math notation="LaTeX">{I} _{D} </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">{V} _{G} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">{R} ^{{2}} </tex-math></inline-formula> score as the evaluation tools, our ML-RFR model infers with an <inline-formula> <tex-math notation="LaTeX">{R} ^{{2}} </tex-math></inline-formula> 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.