AI RESEARCH
WarPGNN: A Parametric Thermal Warpage Analysis Framework with Physics-aware Graph Neural Network
arXiv CS.LG
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ArXi:2603.18581v1 Announce Type: cross With the advent of system-in-package (SiP) chiplet-based design and heterogeneous 2.5D/3D integration, thermal-induced warpage has become a critical reliability concern. While conventional numerical approaches can deliver highly accurate results, they often incur prohib- itively high computational costs, limiting their scalability for complex chiplet-package systems. In this paper, we present WarPGNN, an ef- ficient and accurate parametric thermal warpage analysis framework powered by Graph Neural Networks (GNNs.