APA
Click to copy
Kushwaha, S., Soleimani, N., Treviso, F., Kumar, R., Trinchero, R., Canavero, F., … Sharma, R. (2022). Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects. IEEE Transactions on Electromagnetic Compatibility (Print).
Chicago/Turabian
Click to copy
Kushwaha, Suyash, N. Soleimani, F. Treviso, Rahul Kumar, R. Trinchero, F. Canavero, Sourajeet Roy, and Rohit Sharma. “Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects.” IEEE transactions on electromagnetic compatibility (Print) (2022).
MLA
Click to copy
Kushwaha, Suyash, et al. “Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects.” IEEE Transactions on Electromagnetic Compatibility (Print), 2022.
BibTeX Click to copy
@article{suyash2022a,
title = {Comparative Analysis of Prior Knowledge-Based Machine Learning Metamodels for Modeling Hybrid Copper–Graphene On-Chip Interconnects},
year = {2022},
journal = {IEEE transactions on electromagnetic compatibility (Print)},
author = {Kushwaha, Suyash and Soleimani, N. and Treviso, F. and Kumar, Rahul and Trinchero, R. and Canavero, F. and Roy, Sourajeet and Sharma, Rohit}
}
In this article, machine learning (ML) metamodels have been developed in order to predict the per-unit-length parameters of hybrid copper–graphene on-chip interconnects based on their structural geometry and layout. ML metamodels within the context of this article include artificial neural networks, support vector machines (SVMs), and least-square SVMs. The salient feature of all these ML metamodels is that they exploit the prior knowledge of the p.u.l. parameters of the interconnects obtained from cheap empirical models to reduce the number of expensive full-wave electromagnetic (EM) simulations required to extract the training data. Thus, the proposed ML metamodels are referred to as prior knowledge-based machine learning (PKBML) metamodels. The PKBML metamodels offer the same accuracy as conventional ML metamodels trained exclusively by full-wave EM solver data, but at the expense of far smaller training time costs. In this article, detailed comparative analysis of the proposed PKBML metamodels have been performed using multiple numerical examples.