NeuralMesh: Neural Network For FEM Mesh Generation in 2.5D/3D Chiplet Thermal Simulation

Advanced integrated circuit (IC) systems increasingly utilize chiplet-based packaging with complex $2.5 \mathrm{D} / 3 \mathrm{D}$ structures and dense Through-Silicon Via (TSV) arrays. While the Finite Element Method (FEM) provides high-fidelity thermal simulation for these systems, its computational efficiency degrades significantly when generating and optimizing meshes for intricate geometries. To address these performance limitations while preserving simulation accuracy, we present NeuralMesh, a novel framework that accelerates thermal analysis of chiplet-based ICs. Our approach integrates deep learning and geometric analysis to optimize mesh generation without the need for iterative refinement steps. NeuralMesh first employs an enhanced segmentation model to predict thermal distributions based on geometric, material, and power parameters. These predictions, combined with key geometric features, guide the optimization of an initial coarse FEM mesh. By eliminating traditional iterative mesh refinement, our framework achieves up to $45.00 \times$ mesh generation speedup while maintaining thermal accuracy within 0.8% of commercial COMSOL simulations. It reduces the number of mesh elements in unimportant areas, which represents a speed improvement of the subsequent thermal simulation. This advancement enables rapid yet precise thermal analysis essential for modern IC package design.