Variability-Aware Performance Assessment of Multi-Walled Carbon Nanotube Interconnects using a Predictor-Corrector Polynomial Chaos Scheme

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.