Abstract:
In this article, machine learning (ML) metamodels
have been developed in order to predict the per-unit-length parameters of hybrid copper–graphene on-chip interconnects based
on their structural geometry and layout. ML metamodels within
the context of this article include artificial neural networks, support vector machines (SVMs), and least-square SVMs. The salient
feature of all these ML metamodels is that they exploit the prior
knowledge of the p.u.l. parameters of the interconnects obtained
from cheap empirical models to reduce the number of expensive
full-wave electromagnetic (EM) simulations required to extract the
training data. Thus, the proposedML metamodels are referred to as
prior knowledge-based machine learning (PKBML) metamodels.
The PKBML metamodels offer the same accuracy as conventional
ML metamodels trained exclusively by full-wave EM solver data,
but at the expense of far smaller training time costs. In this article,
detailed comparative analysis of the proposed PKBML metamodels
have been performed using multiple numerical examples.
