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dc.contributor.authorDimple, K M-
dc.contributor.authorGuglani, S-
dc.contributor.authorDasgupta, A-
dc.contributor.authorSharma, R-
dc.contributor.authorRoy, S-
dc.contributor.authorKaushik, B K-
dc.date.accessioned2024-07-02T16:37:51Z-
dc.date.available2024-07-02T16:37:51Z-
dc.date.issued2024-07-02-
dc.identifier.urihttp://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4650-
dc.description.abstractAbstract: In this article, a modified knowledge-based artificial neural network (KBANN) metamodel is developed for the efficient uncertainty quantification of on-chip multiwalled carbon nanotube (MWCNT) interconnects. The proposed KBANN metamodel utilizes the notion of control variates to enable much faster training than what is possible with standard KBANNs. Importantly, techniques to calculate the optimal value of the control variates in an a priori manner without augmenting the training dataset have been developed in this article. Furthermore, techniques to exploit the control variates depending on whether one or multiple low-fidelity models of the MWCNT interconnects are available have also been developed in this article. The benefits of the proposed KBANN metamodel using control variates over standard KBANN metamodels have been validated using multiple MWCNT interconnect examples spanning multiple technology nodes.en_US
dc.language.isoen_USen_US
dc.subjectficial neural networks (ANNs)en_US
dc.subjectcontrol variatesen_US
dc.subjectmultiwalled carbon nanotubes (MWCNTs)en_US
dc.subjecton-chip interconnectsen_US
dc.subjectsignal integrity (SI) analysisen_US
dc.subjectuncertainty quantification (UQ)en_US
dc.titleModified Knowledge-Based Neural Networks Using Control Variates for the Fast Uncertainty Quantification of On-Chip MWCNT Interconnectsen_US
dc.typeArticleen_US
Appears in Collections:Year-2023

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