HC-PINN: A Hard-Constraint Enhanced PINN for Accurate Device-Level Thermal Simulation

In this work, a hard-constraint (HC) enhanced Physics-Informed Neural Network (PINN) framework for accurate device-level thermal simulations is developed, effectively addressing challenges such as complex boundary conditions, multi-material systems, and interface continuity constraints. The approach integrates the domain decomposition method and the mixed residual method (MIM) while enforcing boundary and interface conditions through a hard-constraint strategy, ensuring an improved simulation accuracy. The proposed enhanced PINN thermal simulation framework is verified through its application to advanced silicon-on-insulator (SOI) MOSFETs. Compared with conventional PINN and extended physics-informed neural networks (XPINN) methods, the approach achieves a 4.76× improvement in the MAPE of temperature predictions and significantly enhances the heat flux continuity at material interfaces, ensuring better adherence to physical laws. This work provides a scalable and high-fidelity solution for complex device-level thermal simulations and future semiconductor thermal evaluation and optimization.