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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Bibliographic Details
Published in:2023 7th IEEE Electron Devices Technology & Manufacturing Conference (EDTM) pp. 1 - 3
Main Authors: Sheelvardhan, Kumar, Guglani, Surila, Ehteshamuddin, M., Roy, Sourajeet, Dasgupta, Avirup
Format: Conference Proceeding
Language:English
Published: IEEE 07.03.2023
Subjects:
Artificial neural networks
Computational modeling
Computer architecture
Field effect transistors
Knowledge based systems
Machine learning
Training
Online Access:Get full text
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Summary: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