  In this paper, two methodologies are used to speed up the maximization of the
breakdown volt-age (BV) of a vertical GaN diode that has a theoretical maximum
BV of ~2100V. Firstly, we demonstrated a 5X faster accurate simulation method
in Technology Computer-Aided-Design (TCAD). This allows us to find 50% more
numbers of high BV (>1400V) designs at a given simulation time. Secondly, a
machine learning (ML) model is developed using TCAD-generated data and used as
a surrogate model for differential evolution optimization. It can inversely
design an out-of-the-training-range structure with BV as high as 1887V (89% of
the ideal case) compared to ~1100V designed with human domain expertise.
