## 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 |
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頁（從 - 到） | 5490 - 5497 |

頁數 | 8 |

期刊 | IEEE Transactions on Electron Devices |

卷 | 68 |

發行號 | 11 |

DOIs | |

出版狀態 | Published - 11月 2021 |