Variability Aware FET Model With Physics Knowledge Based Machine Learning

Journal article

Kumar Sheelvardhan, Surila Guglani, M. Ehteshamuddin, Sourajeet Roy, A. Dasgupta
IEEE Electron Devices Technology and Manufacturing Conference, 2023

Semantic Scholar DOI

Cite

APA Click to copy
Sheelvardhan, K., Guglani, S., Ehteshamuddin, M., Roy, S., & Dasgupta, A. (2023). Variability Aware FET Model With Physics Knowledge Based Machine Learning. IEEE Electron Devices Technology and Manufacturing Conference.

Chicago/Turabian Click to copy
Sheelvardhan, Kumar, Surila Guglani, M. Ehteshamuddin, Sourajeet Roy, and A. Dasgupta. “Variability Aware FET Model With Physics Knowledge Based Machine Learning.” IEEE Electron Devices Technology and Manufacturing Conference (2023).

MLA Click to copy
Sheelvardhan, Kumar, et al. “Variability Aware FET Model With Physics Knowledge Based Machine Learning.” IEEE Electron Devices Technology and Manufacturing Conference, 2023.

BibTeX Click to copy

@article{kumar2023a,
  title = {Variability Aware FET Model With Physics Knowledge Based Machine Learning},
  year = {2023},
  journal = {IEEE Electron Devices Technology and Manufacturing Conference},
  author = {Sheelvardhan, Kumar and Guglani, Surila and Ehteshamuddin, M. and Roy, Sourajeet and Dasgupta, A.}
}

Abstract

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.