Session

DescriptionAdvances in machine learning are enabling new approaches to modeling and optimizing the physical behavior of semiconductor devices, circuits, and architectures. This session presents techniques that integrate learning with physics-based simulation, design space exploration, and circuit optimization. The papers explore graph learning, physics-informed neural networks, multi-physics modeling, and AI-driven search methods to improve scalability and accuracy across the physical design and device modeling stack.