Variability Aware FET Model With Physics Knowledge Based Machine Learning

We present variability-aware, computationally efficient, models for Fin Field Effect Transistors (FinFETs) using various machine learning (ML) architectures. This paper provides a detailed comparison of the various architectures. Our physics knowledge-based artificial neural networks (ANNs) demonstr...

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Vydáno v:	2023 7th IEEE Electron Devices Technology & Manufacturing Conference (EDTM) s. 1 - 3
Hlavní autoři:	Sheelvardhan, Kumar, Guglani, Surila, Ehteshamuddin, M., Roy, Sourajeet, Dasgupta, Avirup
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 07.03.2023
Témata:	Artificial neural networks Computational modeling Computer architecture Field effect transistors Knowledge based systems Machine learning Training
On-line přístup:	Získat plný text
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Popis
Shrnutí:	We present variability-aware, computationally efficient, models for Fin Field Effect Transistors (FinFETs) using various machine learning (ML) architectures. This paper provides a detailed comparison of the various architectures. Our physics knowledge-based artificial neural networks (ANNs) demonstrate unprecedented modeling efficiency. This is the first work presenting Prior Knowledge with Input Difference (PKID) ANN architecture for device modeling.
DOI:	10.1109/EDTM55494.2023.10103099