@inproceedings{40b0c5a79a4b4f62aa6f128fe2af46d3,
title = "Self-Attention to Operator Learning-based 3D-IC Thermal Simulation",
abstract = "Thermal management in 3D ICs is increasingly challenging due to higher power densities. Traditional PDESolving 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 UNet Fourier Neural Operator (SAU-FNO), a novel framework combining self-attention and U-Net with FNO to capture longrange 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 SAUFNO achieves state-of-the-art thermal prediction accuracy and provides an 842 × speedup over traditional FEM methods, making it an efficient tool for advanced 3D IC thermal simulations.",
keywords = "Fourier Neural Operator, Self-Attention, Thermal modeling, Transfer Learning",
author = "Zhen Huang and Hong Wang and Wenkai Yang and Muxi Tang and Depeng Xie and Lin, \{Ting Jung\} and Yu Zhang and Xing, \{Wei W.\} and Lei He",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 62nd ACM/IEEE Design Automation Conference, DAC 2025 ; Conference date: 22-06-2025 Through 25-06-2025",
year = "2025",
doi = "10.1109/DAC63849.2025.11132988",
language = "English",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 62nd ACM/IEEE Design Automation Conference, DAC 2025",
address = "United States",
}