IC-ThermBench：用于通用2.5D/3D-IC热学学习的开放渐进式基准
IC-ThermBench: An Open, Progressive Benchmark for Generalizable 2.5D/3D-IC Thermal Learning
AI总结：
IC-ThermBench是含5万样本2.5D小芯片扩展的开放渐进式基准，评估5个泛化范围，经8个基线验证，跨分布封装迁移性能下降显著，目标域自适应可提升性能，支持热预测器公平比较。
AI中文摘要：
标准化基准是电子设计自动化（EDA）领域人工智能可靠进步的基础，包括基于学习的热建模。然而，现有热预测研究常依赖不同的数据集、模拟器、数据划分、预处理流程和指标，且多数数据集和实现代码仍不可用，导致公平且可复现的比较难以开展。本文提出IC-ThermBench，一个开放且渐进式的基准，它结合了已有的3D-IC稳态、瞬态及工业封装任务，以及新的5万样本2.5D小芯片扩展任务，旨在评估更广泛的物理变异和跨分布（OOD）封装迁移能力。该基准涵盖五个泛化范围：3D-IC固定设计预测、布局/材料/边界条件变异下的族内泛化，以及对未见封装系统的跨分布（OOD）封装迁移。我们在统一数据划分、标签和指标下评估了八个代表性基线。随着物理覆盖范围从S2扩展到S4，性能逐渐下降，但跨分布（OOD）封装迁移（S5）导致性能下降幅度大得多：S4时最佳均方根误差（RMSE）和平均绝对误差（MAE）分别为1.216和0.938K，到S5时分别升至15.99和15.00K。当每个跨分布（OOD）案例仅使用10个标记样本时，目标域自适应将最佳MAE降至2.60K。IC-ThermBench还提供了统一的生成、训练、推理和评估流程，支持现有及新型热预测器的可复现、公平比较。
英文摘要：
Standardized benchmarks are fundamental to reliable progress in AI for EDA, including learning-based thermal modeling. However, existing thermal prediction studies often rely on different datasets, simulators, data splits, preprocessing pipelines, and metrics, while most datasets and implementations remain unavailable, making fair and reproducible comparison difficult. We introduce IC-ThermBench, an open and progressive benchmark that combines established 3D-IC steady-state, transient, and industrial package tasks with a new 50,000-sample 2.5D chiplet extension designed to evaluate progressively broader represented physical variation and cross-package OOD transfer. Five Generalization Scopes cover 3D-IC fixed-design prediction, Within-Family Generalization under layout, material, and boundary-condition variation, and Cross-Package OOD transfer to unseen package systems. We evaluate eight representative baselines under common data, splits, labels, and metrics. Performance degrades gradually from S2 to S4 as represented physical support broadens, but Cross-Package OOD produces a much sharper degradation: the best RMSE and MAE increase from 1.216 and 0.938~K at S4 to 15.99 and 15.00~K at S5, respectively. With only 10 labeled samples per OOD case, target-domain adaptation reduces the best MAE to 2.60~K. IC-ThermBench further provides a unified generation, training, inference, and evaluation pipeline, enabling reproducible and fair comparison of existing and new thermal predictor.
