发表机构
Cadence Design Systems; University of Notre Dame（楷登电子; 圣母大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出DAIST，一种可组合的AI加速迭代热求解器，通过域分解和神经算子实现块级重用，在多芯片系统上达178倍加速且误差低于0.33%。
AI 中文摘要
对异构2.5D/3D-IC封装进行精确热分析至关重要，但计算成本高昂。单个全封装有限元模拟可能需要数小时，而基于AI的替代模型将整个堆叠视为单一预测目标，每当芯片数量或拓扑结构变化时都必须重新训练。为解决这一局限，本工作提出了域分解AI加速迭代热分析求解器（DAIST），一种可组合的热求解器，它将全局封装模拟分解为块级子域问题，用神经算子替代子域求解器，并通过界面温度和热通量的迭代交换进行耦合。这种局部到全局的架构消除了整体模型的拓扑锁定：块级神经算子可直接在未见过的封装组合中重用，无需重新训练。迭代耦合策略进一步提供了可控的精度-运行时间权衡，可调整迭代预算以换取运行时间。在多芯片系统和高阶封装系统上评估，DAIST相比传统有限元求解器实现了高达178倍的加速，平均温度误差分别为0.068%和0.323%，同时展示了跨拓扑重用块级模型于结构不同的封装组合的能力。
英文摘要
Accurate thermal analysis of heterogeneous 2.5D/3D-IC packages is essential yet computationally prohibitive. A single full-package FEM simulation can take hours, while AI-based surrogates treat the entire stack as a monolithic prediction target and must be retrained whenever the die count or topology changes. To address this limitation, this work proposes Domain-Decomposed AI-Accelerated Iterative Solver for Thermal Analysis (DAIST), a composable thermal solver that decomposes the global package simulation into block-level subdomain problems, replaces subdomain solvers with neural operators, and couples them through iterative exchanges of interfacial temperature and heat flux. This local-to-global architecture eliminates the topology lock-in of monolithic models: block-level neural operators can be directly reused in unseen package assemblies without retraining. The iterative coupling strategy further provides a controllable accuracy-runtime tradeoff, where the iteration budget can be adjusted to trade accuracy for runtime. Evaluated on a multi-chiplet system and an advanced packaging system, DAIST achieves up to $178\times$ speedup over traditional FEM solvers with mean temperature errors of 0.068% and 0.323%, respectively, while demonstrating cross-topology reuse of block-level models across structurally distinct package assemblies.
