Self-Attention to Operator Learning-based 3D-IC Thermal Simulation
This addresses thermal management challenges for 3D IC design, offering a significant speed improvement over existing methods.
The paper tackles thermal simulation in 3D ICs by introducing SAU-FNO, a framework combining self-attention, U-Net, and FNO, which achieves state-of-the-art accuracy and an 842x speedup over traditional FEM methods.
Thermal management in 3D ICs is increasingly challenging due to higher power densities. Traditional PDE-solving-based methods, while accurate, are too slow for iterative design. Machine learning approaches like FNO provide faster alternatives but suffer from high-frequency information loss and high-fidelity data dependency. We introduce Self-Attention U-Net Fourier Neural Operator (SAU-FNO), a novel framework combining self-attention and U-Net with FNO to capture long-range dependencies and model local high-frequency features effectively. Transfer learning is employed to fine-tune low-fidelity data, minimizing the need for extensive high-fidelity datasets and speeding up training. Experiments demonstrate that SAU-FNO achieves state-of-the-art thermal prediction accuracy and provides an 842x speedup over traditional FEM methods, making it an efficient tool for advanced 3D IC thermal simulations.