Machine Learning Augmented Compact Modeling for Simultaneous Improvement in Computational Speed and Accuracy

Journal article

Kumar Sheelvardhan, Surila Guglani, M. Ehteshamuddin, Sourajeet Roy, A. Dasgupta
IEEE Transactions on Electron Devices, 2024

Semantic Scholar DOI

Cite

APA Click to copy
Sheelvardhan, K., Guglani, S., Ehteshamuddin, M., Roy, S., & Dasgupta, A. (2024). Machine Learning Augmented Compact Modeling for Simultaneous Improvement in Computational Speed and Accuracy. IEEE Transactions on Electron Devices.

Chicago/Turabian Click to copy
Sheelvardhan, Kumar, Surila Guglani, M. Ehteshamuddin, Sourajeet Roy, and A. Dasgupta. “Machine Learning Augmented Compact Modeling for Simultaneous Improvement in Computational Speed and Accuracy.” IEEE Transactions on Electron Devices (2024).

MLA Click to copy
Sheelvardhan, Kumar, et al. “Machine Learning Augmented Compact Modeling for Simultaneous Improvement in Computational Speed and Accuracy.” IEEE Transactions on Electron Devices, 2024.

BibTeX Click to copy

@article{kumar2024a,
  title = {Machine Learning Augmented Compact Modeling for Simultaneous Improvement in Computational Speed and Accuracy},
  year = {2024},
  journal = {IEEE Transactions on Electron Devices},
  author = {Sheelvardhan, Kumar and Guglani, Surila and Ehteshamuddin, M. and Roy, Sourajeet and Dasgupta, A.}
}

Abstract

In this article, we have presented the use of prior physics knowledge-based artificial neural networks (KBANNs) to improve the simulation speed and accuracy of compact models for circuit simulations. Multiple architectures such as source-difference artificial neural network (SD-ANN), prior knowledge input artificial neural network (PKI-ANN), and prior knowledge input with difference artificial neural network (PKID-ANN) have been developed to evaluate the best KBANN framework for runtime improvement, accuracy improvement as well as development (training) time improvement. In particular, the proposed PKID-ANN is the best KBANN since it matches the accuracy of rigorous physics-based technology computer-aided design (TCAD) solvers while exhibiting the most compact architecture, the fastest learning rate, and the fastest runtime. To the best of our knowledge, we have reported the first PKID-based architecture for device modeling in this article, as well as the fastest (simulation time) artificial neural network (ANN) compact model for FinFETs, which even outperforms conventional ANNs (C-ANNs) and industry standard compact models (BSIM-CMG) while retaining the physics and predictive accuracy. Moreover, the reported model also has the lowest training/development cost among all alternatives.