APA
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Kushwaha, S., Attar, A., Trinchero, R., Canavero, F., Sharma, R., & Roy, S. (2021). Fast Extraction of Per-Unit-Length Parameters of Hybrid Copper-Graphene Interconnects via Generalized Knowledge Based Machine Learning. 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS).
Chicago/Turabian
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Kushwaha, Suyash, Amir Attar, R. Trinchero, F. Canavero, Rohit Sharma, and Sourajeet Roy. “Fast Extraction of Per-Unit-Length Parameters of Hybrid Copper-Graphene Interconnects via Generalized Knowledge Based Machine Learning.” 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) (2021).
MLA
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Kushwaha, Suyash, et al. “Fast Extraction of Per-Unit-Length Parameters of Hybrid Copper-Graphene Interconnects via Generalized Knowledge Based Machine Learning.” 2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2021.
BibTeX Click to copy
@article{suyash2021a,
title = {Fast Extraction of Per-Unit-Length Parameters of Hybrid Copper-Graphene Interconnects via Generalized Knowledge Based Machine Learning},
year = {2021},
journal = {2021 IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)},
author = {Kushwaha, Suyash and Attar, Amir and Trinchero, R. and Canavero, F. and Sharma, Rohit and Roy, Sourajeet}
}
In this paper, a knowledge-based machine learning technique has been presented for estimating the per-unit-length parameters of hybrid copper-graphene interconnect networks. The salient feature of the proposed technique is its ability to be trained using significantly smaller amounts of full-wave electromagnetic (EM) solver data compared to conventional machine learning regression techniques, such as artificial neural networks (ANNs) and support vector machines (SVMs).