@inproceedings{169f578260dd4ff9ad9a5860dce3acd4,
title = "Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains",
abstract = "In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the R2 score and RMSE to witness its competency and the average error is < 4%. We do also discuss the circuit simulation possibility by applying the ANN approach.",
keywords = "artificial neural network, deep learning, gate all around, metal side wall, nanofin, nanosheet",
author = "Sagarika Dash and Yiming Li and Sung, {Wen Li}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2023 ; Conference date: 07-03-2023 Through 10-03-2023",
year = "2023",
doi = "10.1109/EDTM55494.2023.10103042",
language = "English",
series = "7th IEEE Electron Devices Technology and Manufacturing Conference: Strengthen the Global Semiconductor Research Collaboration After the Covid-19 Pandemic, EDTM 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "7th IEEE Electron Devices Technology and Manufacturing Conference",
address = "United States",
}