@inproceedings{30e94d2a35d0428fabdb0318eee00ea3,
title = "Deep Learning Approach to Estimating Work Function Fluctuation of Gate-All-Around Silicon Nanosheet MOSFETs with A Ferroelectric HZO Layer",
abstract = "Highly scaled MOSFETs are suffering from various fluctuations. In this paper, an artificial neural network (ANN) device modeling technique is reported for gate-all-around silicon nanosheet MOSFETs (GAA Si NS MOSFETs). The well-trained ANN model can rapidly and accurately estimate the effect of work function fluctuation (WKF) on device characteristic. Our model is generic because it can be successfully evaluated on the device with a ferroelectric HZO layer which have material and structural dissimilarity with the GAA NS device.",
keywords = "artificial neural network, deep learning, ferroelectric layer, gate-all-around, HZO, MOSFETs, nanosheet, work function fluctuation",
author = "Rajat Butola and Yiming Li and Kola, {Sekhar Reddy}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022 ; Conference date: 06-03-2022 Through 09-03-2022",
year = "2022",
doi = "10.1109/EDTM53872.2022.9798172",
language = "English",
series = "6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "232--234",
booktitle = "6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022",
address = "美國",
}