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Estimating Per-Unit-Length Resistance Parameter in Emerging Copper-Graphene Hybrid Interconnects via Prior Knowledge based Accelerated Neural Networks

Journal article

Rahul Kumar, S. S. Likith Narayan, Somesh Kumar, Sourajeet Roy, Brajesh Kumar Kaushik, R. Achar, Rohit Sharma
2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2020
Semantic Scholar DOI
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APA   Click to copy
Kumar, R., Narayan, S. S. L., Kumar, S., Roy, S., Kaushik, B. K., Achar, R., & Sharma, R. (2020). Estimating Per-Unit-Length Resistance Parameter in Emerging Copper-Graphene Hybrid Interconnects via Prior Knowledge based Accelerated Neural Networks. 2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS).

Chicago/Turabian   Click to copy
Kumar, Rahul, S. S. Likith Narayan, Somesh Kumar, Sourajeet Roy, Brajesh Kumar Kaushik, R. Achar, and Rohit Sharma. “Estimating Per-Unit-Length Resistance Parameter in Emerging Copper-Graphene Hybrid Interconnects via Prior Knowledge Based Accelerated Neural Networks.” 2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) (2020).

MLA   Click to copy
Kumar, Rahul, et al. “Estimating Per-Unit-Length Resistance Parameter in Emerging Copper-Graphene Hybrid Interconnects via Prior Knowledge Based Accelerated Neural Networks.” 2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2020.

BibTeX   Click to copy 

@article{rahul2020a,
  title = {Estimating Per-Unit-Length Resistance Parameter in Emerging Copper-Graphene Hybrid Interconnects via Prior Knowledge based Accelerated Neural Networks},
  year = {2020},
  journal = {2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)},
  author = {Kumar, Rahul and Narayan, S. S. Likith and Kumar, Somesh and Roy, Sourajeet and Kaushik, Brajesh Kumar and Achar, R. and Sharma, Rohit}
}

Abstract

In this paper, an artificial neural network (ANN) is developed to model how the geometrical parameters of hybrid copper-graphene interconnects affect the per-unit-length resistance values. The proposed ANN is intelligently trained using large amounts of data representing the prior knowledge about the interconnects, extracted from an analytical model and sparse amount of data extracted from a rigorous full-wave electromagnetic solver. In this way, the training of the ANN model is accelerated without significant loss in accuracy.