Journal article
Sakshi Bhatnagar, Amanda Merkley, Rena Berdine, Yingheng Li, Sourajeet Roy
Electrical Design of Advanced Packaging and Systems Symposium, 2018
Electrical Design of Advanced Packaging and Systems Symposium, 2018
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DOI
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APA
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Bhatnagar, S., Merkley, A., Berdine, R., Li, Y., & Roy, S. (2018). Variability-Aware Performance Assessment of Multi-Walled Carbon Nanotube Interconnects using a Predictor-Corrector Polynomial Chaos Scheme. Electrical Design of Advanced Packaging and Systems Symposium.
Chicago/Turabian
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Bhatnagar, Sakshi, Amanda Merkley, Rena Berdine, Yingheng Li, and Sourajeet Roy. “Variability-Aware Performance Assessment of Multi-Walled Carbon Nanotube Interconnects Using a Predictor-Corrector Polynomial Chaos Scheme.” Electrical Design of Advanced Packaging and Systems Symposium (2018).
MLA
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Bhatnagar, Sakshi, et al. “Variability-Aware Performance Assessment of Multi-Walled Carbon Nanotube Interconnects Using a Predictor-Corrector Polynomial Chaos Scheme.” Electrical Design of Advanced Packaging and Systems Symposium, 2018.
BibTeX Click to copy
@article{sakshi2018a,
title = {Variability-Aware Performance Assessment of Multi-Walled Carbon Nanotube Interconnects using a Predictor-Corrector Polynomial Chaos Scheme},
year = {2018},
journal = {Electrical Design of Advanced Packaging and Systems Symposium},
author = {Bhatnagar, Sakshi and Merkley, Amanda and Berdine, Rena and Li, Yingheng and Roy, Sourajeet}
}
Abstract
In this paper, a predictor-corrector scheme is presented to expedite the construction of polynomial chaos (PC) metamodels for the variability-aware performance assessment of multi-walled carbon nanotube (MWCNT) interconnects. The proposed scheme is broken into two main stages. First, a low-fidelity predictor PC metamodel of the MWCNT network is constructed using the equivalent single conductor (ESC) approximation model. Thereafter, the accuracy of the predictor model is sufficiently enriched using a low-order corrector function based on the rigorous multiconductor circuit (MCC) model. The combined CPU costs of constructing the predictor and corrector functions are 9 times smaller than the CPU costs for directly constructing a conventional PC metamodel of comparable accuracy.