GTN-Path: Efficient Path Timing Prediction through Waveform Propagation with Graph Transformer

As technology nodes shrink, static timing analysis (STA) must balance accuracy and efficiency to ensure circuit functionality. Graph-based analysis (GBA) is fast but pessimistic, while path-based analysis (PBA) offers higher accuracy with an expensive runtime cost. However, GBA and PBA rely on lookup table (LUT)-based standard cell libraries, introducing accuracy losses compared to accurate SPICE simulations at advanced technology nodes. This work presents GTN-Path, an efficient post-layout path timing prediction method based on waveform propagation and graph transformer network (GTN). GTN-Path captures structural information to accurately predict waveforms by modeling standard cells and interconnects as graphs. Compared to HSPICE simulations, GTN-Path predicts waveforms with $2.98 \%$ error and delay with $2.96 \%$ error, achieving a speedup of $3510 \times$. Additionally, compared with the sign-off STA tool, the GTN-Path achieves a speedup of $12 \times$.