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

Chandni Akbar, Yiming Li, Wen Li Sung

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)5490 - 5497
Number of pages8
JournalIEEE Transactions on Electron Devices
Volume68
Issue number11
DOIs
StatePublished - Nov 2021

Keywords

  • Data models
  • Gallium arsenide
  • Gate-all-around (GAA)
  • machine learning (ML)
  • MOSFET
  • nanosheet (NS)
  • Predictive models
  • random forest regressor (RFR)
  • Semiconductor device modeling
  • Silicon
  • Solid modeling
  • work function fluctuation (WKF).

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