  Compact semiconductor device models are essential for efficiently designing
and analyzing large circuits. However, traditional compact model development
requires a large amount of manual effort and can span many years. Moreover,
inclusion of new physics (eg, radiation effects) into an existing compact model
is not trivial and may require redevelopment from scratch. Machine Learning
(ML) techniques have the potential to automate and significantly speed up the
development of compact models. In addition, ML provides a range of modeling
options that can be used to develop hierarchies of compact models tailored to
specific circuit design stages. In this paper, we explore three such options:
(1) table-based interpolation, (2)Generalized Moving Least-Squares, and (3)
feed-forward Deep Neural Networks, to develop compact models for a p-n junction
diode. We evaluate the performance of these "data-driven" compact models by (1)
comparing their voltage-current characteristics against laboratory data, and
(2) building a bridge rectifier circuit using these devices, predicting the
circuit's behavior using SPICE-like circuit simulations, and then comparing
these predictions against laboratory measurements of the same circuit.
