APA
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Li, Y., Bhatnagar, S., Merkely, A., Weber, D. J., & Roy, S. (2019). A Predictor–Corrector Algorithm for Fast Polynomial Chaos-Based Uncertainty Quantification of Multi-Walled Carbon Nanotube Interconnects. IEEE Transactions on Components, Packaging, and Manufacturing Technology.
Chicago/Turabian
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Li, Yingheng, Sakshi Bhatnagar, Amanda Merkely, David J. Weber, and Sourajeet Roy. “A Predictor–Corrector Algorithm for Fast Polynomial Chaos-Based Uncertainty Quantification of Multi-Walled Carbon Nanotube Interconnects.” IEEE Transactions on Components, Packaging, and Manufacturing Technology (2019).
MLA
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Li, Yingheng, et al. “A Predictor–Corrector Algorithm for Fast Polynomial Chaos-Based Uncertainty Quantification of Multi-Walled Carbon Nanotube Interconnects.” IEEE Transactions on Components, Packaging, and Manufacturing Technology, 2019.
BibTeX Click to copy
@article{yingheng2019a,
title = {A Predictor–Corrector Algorithm for Fast Polynomial Chaos-Based Uncertainty Quantification of Multi-Walled Carbon Nanotube Interconnects},
year = {2019},
journal = {IEEE Transactions on Components, Packaging, and Manufacturing Technology},
author = {Li, Yingheng and Bhatnagar, Sakshi and Merkely, Amanda and Weber, David J. and Roy, Sourajeet}
}
In this article, a predictor–corrector algorithm for the fast polynomial chaos (PC)-based uncertainty quantification (UQ) of multi-walled carbon nanotube (MWCNT) interconnect networks is presented. The proposed algorithm intelligently combines the numerical efficiency of the approximate equivalent single conductor (ESC) model of the MWCNT interconnect network with the rigor and accuracy of a multi-conductor circuit (MCC) model. Consequently, this algorithm significantly accelerates the generation of the PC surrogate models (or metamodels) of the network responses for minimal loss in accuracy. These metamodels can be probed efficiently and repeatedly to quantify the impact of manufacturing and fabrication process uncertainty on the MWCNT network responses.