Deep Learning Approach to Estimating Work Function Fluctuation of Gate-All-Around Silicon Nanosheet MOSFETs with A Ferroelectric HZO Layer

Rajat Butola, Yiming Li*, Sekhar Reddy Kola

*此作品的通信作者

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面232-234
頁數3
ISBN(電子)9781665421775
DOIs
出版狀態Published - 2022
事件6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022 - Virtual, Online, Japan
持續時間: 6 3月 20229 3月 2022

出版系列

名字6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022

Conference

Conference6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022
國家/地區Japan
城市Virtual, Online
期間6/03/229/03/22

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