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

Chandni Akbar, Yiming Li, Wen Li Sung

研究成果: Article同行評審

14 引文 斯高帕斯(Scopus)

摘要

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-VG 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 R² score as the evaluation tools, our ML-RFR model infers with an R² 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.

原文English
頁(從 - 到)5490 - 5497
頁數8
期刊IEEE Transactions on Electron Devices
68
發行號11
DOIs
出版狀態Published - 11月 2021

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