WarPGNN: A Parametric Thermal Warpage Analysis Framework with Physics-aware Graph Neural Network
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 prohibitively high computational costs, limiting their scalability for complex chiplet-package systems. In this paper, we present WarPGNN, an efficient and accurate parametric thermal warpage analysis framework powered by Graph Neural Networks (GNNs). By operating directly on graphs constructed from the floorplans, WarPGNN enables fast warpage-aware floorplan exploration and exhibits strong transferability across diverse package ...
| Authors | Haotian Lu, Jincong Lu, Sachin Sachdeva et al. |
|---|---|
| Primary category | cs.AR |
| Published | 19 March 2026 |
| Last updated at source | 21 June 2026 |
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