Abstract:
Exploring high-dimensional semiconductor device design spaces under coupled electrothermal constraints remains challenging, particularly in 3-D heterogeneous systems with tightly coupled electrical, thermal, and integration requirements. Conventional TCAD-driven exploration is physically accurate but computationally expensive and offers limited insight into governing parameters. This work presents physics-guided active learning trigate optimization (PALTO), …