Presentation
PG-GNN: Physics-Guided Graph Neural Network for Cell Timing Library Characterization
DescriptionStandard cell library characterization is a critical bottleneck for timing closure. Existing machine learning (ML) surrogates reduce SPICE costs but are constrained by labeled data requirements, limiting accuracy. This work introduces PG-GNN, a Physics-Guided Graph Neural Network (GNN) framework integrating residual learning with uncertainty-driven active learning. It simulates sparse anchor PVT corners, builds a physics-consistent reference, and trains a GNN on residual errors. Active learning then queries high-uncertainty points to minimize labeling. In experiments on TSMC 16 nm libraries, PG-GNN reduces SPICE effort by 98.7% with 1.67% MAPE. When applied to ISCAS'89 benchmarks, it achieves 0.74% critical path delay mismatch versus foundry libraries. PG-GNN offers orders-of-magnitude speedups in characterization runtime while maintaining signoff-level accuracy, presenting a scalable solution for next-generation IC design flows.
Event Type
Research Manuscript
TimeMonday, July 2712:03pm - 12:16pm PDT
LocationMtg Room 202AB
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