T-Fusion: Thermal Modeling of 3D ICs with Multi-fidelity Fusion
Bingrui Zhang, Wei W. Xing, Xin Zhao, Yuquan Sun · 2025
In the post-Moore era, three-dimensional integrated circuit (3D-IC) technology is a key direction for continuing to enhance chip performance. However, in the thermal simulation field, existing works either only address two-dimensional temperature fields or require a large number of samples and a long training time to train the model. In order to meet the current demand in chip design for rapid and accurate thermal prediction with limited sample sizes, this paper introduces a multi-fidelity model, T-Fusion, which combines tensor arithmetic and Bayesian autoregression. Leveraging a sparse set of high-fidelity data alongside abundant low-fidelity samples, T-Fusion reliably estimates high-fidelity thermal distribution across the chip. We validate our model on single-core double-layer chips, quad-core triple-layer, and octa-core double-layer chips respectively. We compare the predicted heat distribution with commercial thermal simulation software such as COMSOL, MTA, and Hotspot, achieving accelerations of 10,000x to 1,000,000x. T-fusion can also be applied to transient temperature prediction of the chip, requiring only 20 sets of high-precision data and 64 sets of low-precision data to control ME under 1K.