Deep-Learning-Assisted Physics-Driven MOSFET Current-Voltage Modeling

Ming Yen Kao*, H. Kam, Chenming Hu

*此作品的通信作者

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

In this work, we propose using deep learning to improve the accuracy of the partially-physics-based conventional MOSFET current-voltage model. The benefits of having some physics-driven features in the model are discussed. Using a portion of the Berkeley Short-channel IGFET Common-Multi-Gate (BSIM-CMG), the industry-standard FinFET and GAAFET compact model, as the physics model and a 3-layer neural network with 6 neurons per layer, the resultant model can well predict IV, output conductance, and transconductance of a TCAD-simulated gate-all-around transistor (GAAFET) with outstanding 3-sigma errors of 1.3%, 4.1%, and 2.9%, respectively. Implications for circuit simulation are also discussed.

原文English
頁(從 - 到)974-977
頁數4
期刊Ieee Electron Device Letters
43
發行號6
DOIs
出版狀態Published - 1 6月 2022

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