CMAS Lab - Noise-Aware Uncertainty Quantification of MLGNR

Journal article

Asha Kumari Jakhar, Surila Guglani, Avirup Dasgupta, Sourajeet Roy
Electrical Design of Advanced Packaging and Systems Symposium, 2023

Semantic Scholar DOI

Cite

APA Click to copy
Jakhar, A. K., Guglani, S., Dasgupta, A., & Roy, S. (2023). Noise-Aware Uncertainty Quantification of MLGNR Interconnects using Fast Trained Artificial Neural Networks. Electrical Design of Advanced Packaging and Systems Symposium.

Chicago/Turabian Click to copy
Jakhar, Asha Kumari, Surila Guglani, Avirup Dasgupta, and Sourajeet Roy. “Noise-Aware Uncertainty Quantification of MLGNR Interconnects Using Fast Trained Artificial Neural Networks.” Electrical Design of Advanced Packaging and Systems Symposium (2023).

MLA Click to copy
Jakhar, Asha Kumari, et al. “Noise-Aware Uncertainty Quantification of MLGNR Interconnects Using Fast Trained Artificial Neural Networks.” Electrical Design of Advanced Packaging and Systems Symposium, 2023.

BibTeX Click to copy

@article{asha2023a,
  title = {Noise-Aware Uncertainty Quantification of MLGNR Interconnects using Fast Trained Artificial Neural Networks},
  year = {2023},
  journal = {Electrical Design of Advanced Packaging and Systems Symposium},
  author = {Jakhar, Asha Kumari and Guglani, Surila and Dasgupta, Avirup and Roy, Sourajeet}
}

Abstract

This paper presents a prior knowledge accelerated transfer learning technique to efficiently train artificial neural networks (ANNs) to address uncertainty in on-chip multilayer graphene nanoribbon (MLGNR) interconnects. The salient feature of the proposed training technique is that it accounts for the variation in the amplitude, frequency, and phase angle of the power supply noise and ground bounce signals. Consequently, the resultant ANN model is noise-aware and can be used to anticipate the eye diagram attributes of the MLGNR interconnects for any arbitrary noise signal without the need for any retraining.