Fast extraction of per-unit-length parameters of hybrid copper-graphene interconnects via generalized knowledge based machine learning

dc.contributor.author	Kushwaha, S.
dc.contributor.author	Attar, A.
dc.contributor.author	Trinchero, R.
dc.contributor.author	Canavero, F.
dc.contributor.author	Sharma, R.
dc.contributor.author	Roy, S.
dc.date.accessioned	2022-08-26T17:14:22Z
dc.date.available	2022-08-26T17:14:22Z
dc.date.issued	2022-08-26
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/3933
dc.description.abstract	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).	en_US
dc.language.iso	en_US	en_US
dc.subject	Artificial neural network (ANN)	en_US
dc.subject	Copper-graphene hybrid interconnects	en_US
dc.subject	Per-unit-length parameters	en_US
dc.subject	Support vector machine (SVM)	en_US
dc.subject	Training data	en_US
dc.subject	Variability analysis	en_US
dc.title	Fast extraction of per-unit-length parameters of hybrid copper-graphene interconnects via generalized knowledge based machine learning	en_US
dc.type	Article	en_US