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

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.