APA
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Bhatnagar, S., Li, Y., Merkely, A., Weber, D. J., & Roy, S. (2019). Predictor-Corrector Algorithms and Their Scalability Analysis for Fast Stochastic Modeling of Multi-Walled Carbon Nanotube Interconnects. 2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility (EMC Sapporo/APEMC).
Chicago/Turabian
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Bhatnagar, Sakshi, Yingheng Li, Amanda Merkely, David J. Weber, and Sourajeet Roy. “Predictor-Corrector Algorithms and Their Scalability Analysis for Fast Stochastic Modeling of Multi-Walled Carbon Nanotube Interconnects.” 2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility (EMC Sapporo/APEMC) (2019).
MLA
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Bhatnagar, Sakshi, et al. “Predictor-Corrector Algorithms and Their Scalability Analysis for Fast Stochastic Modeling of Multi-Walled Carbon Nanotube Interconnects.” 2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility (EMC Sapporo/APEMC), 2019.
BibTeX Click to copy
@article{sakshi2019a,
title = {Predictor-Corrector Algorithms and Their Scalability Analysis for Fast Stochastic Modeling of Multi-Walled Carbon Nanotube Interconnects},
year = {2019},
journal = {2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility (EMC Sapporo/APEMC)},
author = {Bhatnagar, Sakshi and Li, Yingheng and Merkely, Amanda and Weber, David J. and Roy, Sourajeet}
}
In this paper, a predictor-corrector algorithm for the fast stochastic analysis of multi-walled carbon nanotube (MWCNT) interconnect networks is proposed. This algorithm begins by developing a polynomial chaos (PC) predictor metamodel to capture the coarse features in the stochastic network response. In order to expedite the construction of the predictor metamodel, the compact but approximate equivalent single conductor (ESC) model of the network is used. Thereafter, the finer features of the stochastic network response are captured using a corrector metamodel. This corrector metamodel is formulated using a very sparse set of the rigorous and expensive multi-conductor circuit (MCC) model. The combination of the predictor and corrector metamodels is found to be far more efficient than conventional PC metamodels constructed using the MCC model only. In this paper, the scaling of the efficiency factor with respect to the number of shells in the MWCNT network is quantified.