Lay-Net: Grafting Netlist Knowledge on Layout-Based Congestion Prediction

Congestion modeling is crucial for enhancing the routability of VLSI placement solutions. The underutilization of netlist information constrains the efficacy of existing layout-based congestion modeling techniques. We devise a novel approach that grafts netlist-based message passing (MP) into a layout-based model, thereby achieving a better knowledge fusion between layout and netlist to improve congestion prediction performance. The innovative heterogeneous MP paradigm more effectively incorporates routing demand into the model by considering con nections between cells, overlaps of nets, and interactions between cells and nets. Leveraging multiscale features, the proposed model effectively captures connection information across various ranges, addressing the issue of inadequate global information present in existing models. Using contrastive learning and mini-Gnet techniques allows the model to learn and represent features more effectively, boosting its capabilities and achieving supe rior performance. Extensive experiments demonstrate a notable performance enhancement of the proposed model compared to existing methods.