Data-driven thermal predictors for 3D-ICs are often trained from scratch for each chip design using many high-fidelity finite-element simulations, leading to high data-generation cost and costly cross-design reuse. We propose Therm-FM, a neural operator framework that adapts a pretrained partial differential equation (PDE) foundation model to steady-state and transient 3D-IC thermal simulation. The motivation is that steady-state and transient chip-level heat conduction respectively share elliptic and parabolic operator structures with diffusion-type PDEs, allowing pretrained diffusion priors to provide an effective initialization for thermal-field prediction under heterogeneous materials, dense TSV/microbump interconnects, and package-level boundary conditions. To further reduce data-generation cost, Therm-FM incorporates a thermal-equivalent multi-fidelity training strategy that uses low-cost approximate simulations for thermal-domain adaptation and limited high-fidelity samples for calibration. Experiments on public HotSpot benchmarks and industrial 3D-IC package benchmarks show that Therm-FM achieves up to a 10.6x reduction in mean error and surpasses prior best accuracy with less than 20% of the training data. In cross-chip adaptation, it matches or surpasses full-data baselines in several metrics using only 10--30 target samples. We release datasets, source code, and pretrained models at https://github.com/haiyangxin/Therm-FM.

翻译：面向3D-IC的数据驱动型热预测器通常需针对每个芯片设计，利用大量高保真有限元仿真从零开始训练，导致数据生成成本高昂且跨设计复用困难。我们提出Therm-FM，一种神经算子框架，可将预训练的偏微分方程（PDE）基础模型适配至稳态与瞬态3D-IC热仿真。其动机在于：稳态与瞬态芯片级热传导分别与扩散型PDE共享椭圆及抛物算子结构，使得预训练的扩散先验能够在异质材料、密集TSV/微凸点互连及封装级边界条件下，为热场预测提供有效初始化。为进一步降低数据生成成本，Therm-FM引入热等效多保真训练策略，利用低成本近似仿真进行热域适配，并通过少量高保真样本进行校准。在公共HotSpot基准测试与工业级3D-IC封装基准上的实验表明：Therm-FM可将平均误差降低达10.6倍，且仅需不足20%的训练数据即可超越先前最优精度。在跨芯片适配中，仅使用10~30个目标样本，Therm-FM即在多项指标上达到或超越全数据基线。数据集、源代码及预训练模型已开源至https://github.com/haiyangxin/Therm-FM。