发表机构
National Tsing Hua University（国立清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建开源工艺有限元模型与简化群优化代理的开放流程，用于IGBT模块可靠性设计，经FEA确认和填充降低代理误差，并发布全部资源。
AI 中文摘要
工艺引起的翘曲、陶瓷应力和焊料应变限制了直接键合铜（DBC）基板上绝缘栅双极晶体管（IGBT）模块的可靠性。代理辅助设计研究在有限元分析（FEA）数据库上训练回归模型，但很少针对新的FEA检查优化设计，或报告代理误差与优化器之间的相互作用。本文构建并评估了一个开放流程：一个开源工艺有限元模型、由简化群优化（SSO）调优的代理模型、多目标设计搜索、选定设计的FEA确认以及确认驱动的填充。该模型从第二次回流开始，在回流、封装和成型阶段无需拟合参数即可将实测翘曲分别复现于18.2%、33.6%和15.3%的误差范围内。在一个平衡的60设计数据库上，所有随机调优器达到相同的测试精度，在交叉验证性能上对每个输出均优于已发表的网格，但仅在翘曲方面泛化更好；调优器的交叉验证排名不转移到测试集。在同等结果报告下，多目标SSO和非支配排序遗传算法II给出可比的Pareto前沿；修正的多目标粒子群优化器落后于两者。FEA确认显示翘曲预测成立（平均绝对误差0.5%），而在优化器达到的设计空间边界处，陶瓷应力代理乐观高达26%。两轮确认驱动的填充将该误差降至1-10%，并将样本外误差减半；没有确认的设计在陶瓷应力上优于数据库。陶瓷应力结果具有指示性，因为该指标在生产分辨率下对网格敏感。模型、数据库、脚本以及预注册和后注册结果均已发布。
英文摘要
Process-induced warpage, ceramic stress and solder strain limit the reliability of insulated-gate bipolar transistor (IGBT) modules on direct-bonded copper (DBC) substrates. Surrogate-assisted design studies train regression models on finite-element analysis (FEA) databases, but rarely check the optimized designs against new FEA or report how surrogate error interacts with the optimizer. This paper builds and evaluates an open pipeline: an open-source process finite-element model, surrogates tuned by Simplified Swarm Optimization (SSO), multi-objective design search, FEA confirmation of selected designs and confirmation-driven infill. The model starts at the second reflow and reproduces measured warpage within 18.2%, 33.6% and 15.3% at the reflow, housing and molding stages without fitted parameters. On a balanced 60-design database, all stochastic tuners reach the same test accuracy, outperform the published grid on cross-validated performance for every output but generalize better only for warpage; the cross-validated ranking of tuners does not transfer to the test set. With equal result reporting, multi-objective SSO and the non-dominated sorting genetic algorithm II give comparable Pareto fronts; a corrected multi-objective particle swarm optimizer trails both. FEA confirmation shows that warpage predictions hold (mean absolute error 0.5%), whereas at the design-space bounds reached by the optimizers the ceramic-stress surrogate is optimistic by up to 26%. Two confirmation-driven infill rounds reduce this error to 1-10% and halve the out-of-sample error; no confirmed design improves on the database in ceramic stress. Ceramic-stress results are indicative, as the metric is mesh-sensitive at production resolution. The model, database, scripts and pre-registered and post-registration results are released.
CommentsSubmitted to Engineering Applications of Artificial Intelligence. 41 pages, 8 figures, 14 tables (5 supplementary)
