  An innovative methodology that leverages artificial intelligence (AI) and
graph representation for semiconductor device encoding in TCAD device
simulation is proposed. A graph-based universal encoding scheme is presented
that not only considers material-level and device-level embeddings, but also
introduces a novel spatial relationship embedding inspired by interpolation
operations typically used in finite element meshing. Universal physical laws
from device simulations are leveraged for comprehensive data-driven modeling,
which encompasses surrogate Poisson emulation and current-voltage (IV)
prediction based on drift-diffusion model. Both are achieved using a novel
graph attention network, referred to as RelGAT. Comprehensive technical details
based on the device simulator Sentaurus TCAD are presented, empowering
researchers to adopt the proposed AI-driven Electronic Design Automation (EDA)
solution at the device level.
