Abstract:
In this article, an artificial neural network (ANN) is
developed in order to predict the per-unit-length (p. u. l.) parameters of hybrid copper-graphene on-chip interconnects from a prior
knowledge of their structural geometry and layout. The salient
feature of the proposed ANN is that it combines knowledge of the
p. u. l. parameters extracted from empirical models along with
that extracted from a rigorous full-wave electromagnetic solver. As
a result, the proposed ANN is referred to as a knowledge-based
neural network (KBNN). The KBNN has been found to converge to
the same accuracy as a conventional ANN but at the expense of far
smaller training time costs. As a result, the KBNN is much more
suitable for performing design space explorations.
