GNN-MLS: Signal Routing in Mixed-Node 3D ICs through GNN-Assisted Metal Layer Sharing

Native 3D Integrated Circuit (3D IC) design offers enhanced performance and density but faces challenges in signal routing due to limited true 3D EDA tool support. Pseudo-3D flows bridge this gap but lack cross-tier optimization, critical for both mixed-node and homogeneous designs. Metal Layer Sharing (MLS) addresses this by enabling cross-tier routing co-optimization but risks timing degradation if not applied strategically. Additionally, MLS creates open connections in hybrid-bonded 3D ICs, making chips untestable. We propose GNN-MLS, a Graph Neural Network-based framework for precise MLS net selection, combined with a tailored DFT solution for robust testability. Experiments show GNN-MLS reduces timing violations by 79% and improves WNS and TNS by 81% and 94%, and moves designs closer to true 3D ICs.