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：面向三维集成电路的数据驱动热预测器通常需要针对每个芯片设计从零开始训练，并依赖大量高保真有限元仿真，导致高额的数据生成成本及跨设计复用困难。我们提出Therm-FM神经算子框架，该框架将预训练的偏微分方程基础模型适配至稳态与瞬态三维集成电路热仿真。其核心动机在于：稳态与瞬态芯片级热传导分别与扩散型偏微分方程共享椭圆型与抛物型算子结构，使得预训练的扩散先验能为异质材料、密集硅通孔/微凸点互连及封装级边界条件下的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融合了热等效多保真度训练策略，该策略通过低成本近似仿真完成热域适配，并利用有限高保真样本进行校准。在公开HotSpot基准测试与工业级三维集成电路封装基准上的实验表明：Therm-FM将平均误差降低达10.6倍，且使用不足20%的训练数据即可超越先前最优精度；在跨芯片适配中，仅需10-30个目标样本即可在多项指标上匹配或超越全数据基线。我们已在https://github.com/haiyangxin/Therm-FM 开源数据集、源代码及预训练模型。