Presentation
From Fluid Dynamics to Chip Design: PDE Foundation Model Address Data Bottleneck in 3D-ICs Thermal Simulation
DescriptionMachine learning can reduce 3D-ICs thermal analysis runtime from hours to seconds, yet most existing ML-based thermal models train each chip independently from scratch, demanding large datasets while failing to exploit the mathematical equivalence between heat conduction and diffusion-type PDEs. We demonstrate that foundation neural operators pretrained on diverse PDE families enable effective transfer to 3D-IC thermal simulation. Building on this insight, we develop PNO-Therm, a specialized model obtained through targeted fine-tuning, which surpasses the previous state-of-the-art method using less than 20% of the training data. At equal dataset sizes, our method achieves 6–10× lower MAE, 3.5× reduced GPU memory consumption, and over 3× faster training while maintaining approximately 940× speedup versus FEM solvers. Validated across three representative 3D-IC designs, PNO-Therm establishes that pretrained neural operators provide a scalable pathway for high-accuracy thermal modeling under limited data.
Event Type
Research Manuscript
TimeMonday, July 2712:16pm - 12:30pm PDT
LocationMtg Room 101A