DiffCool: Label-Free Synthesis of Chip-Tailored Heat Sinks via Thermal-Aware Diffusion

Recent advances in semiconductor technology have dramatically improved IC chip performance, but soaring transistor densities and clock speeds generate unprecedented heat fluxes in modern integrated circuits, making spatially adaptive thermal management critical. However, conventional heat sinks rely on fixed geometries that lack customization for chip-specific thermal profiles, while the absence of optimal design datasets and strict manufacturing constraints hinder the application of generative AI. To address these challenges, we present DiffCool, a physics-guided framework that reformulates heat sink synthesis as a constrained discrete denoising process. By embedding manufacturing-aware state transitions into the diffusion dynamics and optimizing via a differentiable thermal-aware loss coupled with a surrogate simulator, our model generates structurally sound, high-performance topologies directly from bare-chip heat maps without labeled data. Experiments on an open-source processor demonstrate that DiffCool synthesizes production-ready designs in under one second, reducing peak temperature by 28.3% and temperature gradient by 56.9% compared to conventional baselines. This work establishes a scalable, label-free paradigm that unifies generative modeling with physical constraints for automated thermal design.