GraphCAD: Leveraging Graph Neural Networks for Accuracy Prediction Handling Crosstalk-affected Delays
Fangzhou Liu, Guannan Guo, Yuyang Ye, Ziyi Wang, Wenjie Fu, Weihua Sheng, Bei Yu · 2025
As chip fabrication technology advances, the capacitive effects between wires have become increasingly pronounced, making crosstalk-induced incremental delay a serious issue. Traditional static timing analysis involves complex and iterative calculations through timing windows, requiring precise alignment of aggressor and victim nets, along with delay and slew estimations, which significantly increase runtime and licensing costs. In our work, we develop a Graph Neural Network framework to predict crosstalk-affected delays, focusing on the impacts of the coupling effect and overlapping nets. Moreover, we employ a curriculum learning strategy that gradually integrates aggressors with victims, improving model convergence through progressively complex scenarios. Experimental results show that our framework precisely predicts crosstalk-affected delays, matching commercial tools' performance with a fivefold speedup.