Predictor-Corrector Algorithm with Embedded Dimension Reduction for Uncertainty Quantification of MWCNT On-Chip Interconnect Networks

Journal article

Surila Guglani, Sourajeet Roy
2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2020

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APA Click to copy
Guglani, S., & Roy, S. (2020). Predictor-Corrector Algorithm with Embedded Dimension Reduction for Uncertainty Quantification of MWCNT On-Chip Interconnect Networks. 2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS).

Chicago/Turabian Click to copy
Guglani, Surila, and Sourajeet Roy. “Predictor-Corrector Algorithm with Embedded Dimension Reduction for Uncertainty Quantification of MWCNT On-Chip Interconnect Networks.” 2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) (2020).

MLA Click to copy
Guglani, Surila, and Sourajeet Roy. “Predictor-Corrector Algorithm with Embedded Dimension Reduction for Uncertainty Quantification of MWCNT On-Chip Interconnect Networks.” 2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), 2020.

BibTeX Click to copy

@article{surila2020a,
  title = {Predictor-Corrector Algorithm with Embedded Dimension Reduction for Uncertainty Quantification of MWCNT On-Chip Interconnect Networks},
  year = {2020},
  journal = {2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)},
  author = {Guglani, Surila and Roy, Sourajeet}
}

Abstract

This paper presents a novel polynomial chaos (PC) approach for the fast uncertainty quantification of on-chip multiwalled carbon nanotube (MWCNT) interconnect networks. The proposed approach combines the benefits of predictor-corrector algorithms with that of dimension reduction strategies to provide two distinct levels of numerical efficiency when training the PC metamodels. As a result, this approach is even better scalable with respect to the number of problem dimensions than conventional predictor-corrector algorithms and state-of-the-art dimension reduction techniques.