Abstract
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.
Abstract (translated)
在三维集成电路（3D ICs）中，热管理由于更高的功率密度而变得越来越具有挑战性。传统的基于偏微分方程(PDE)求解的方法虽然准确，但速度太慢，不适合迭代设计过程。机器学习方法如傅里叶神经网络(FNO)提供了更快的替代方案，但它们在高频信息丢失和高保真数据依赖方面存在问题。 我们引入了一种新的框架——Self-Attention U-Net Fourier Neural Operator (SAU-FNO)，该框架结合了自注意力机制、U-Net架构与FNO，可以有效捕捉长距离依赖性并准确建模局部高频特征。同时采用了迁移学习来对低保真数据进行微调，从而减少了对大规模高保真数据集的需求，并加快了训练过程。 实验结果显示，SAU-FNO在热预测准确性上达到了业界领先水平，并且比传统的有限元方法(FEM)快842倍，成为先进3D IC热模拟中一种高效工具。
URL
https://arxiv.org/abs/2510.15968
