ML-Based Fine-Grained Modeling of DC Current Crowding in Power Delivery TSVs for Face-to-Face 3D ICs

In contrast to uniform distribution in power wires, actual currents tend to exhibit complicated crowding phenomena at the connections between Through-silicon-via (TSV) and power wires. The current crowding effect degrades power integrity and increases the difficulty of 3D IC power delivery network (PDN) analysis. Therefore, a detailed analysis of current distribution and IR drops in power TSVs within 3D IC PDN is important. This paper will explore the complicated current behavior within TSVs and PDNs of the promising face-to-face 3D IC architecture. Since existing simulation methods are computationally intensive and time-consuming, we propose a graph attention network-based (GAT-based) framework, with novel aggregation methods in the GAT models and informative fine-grained graph generation methods, to achieve efficient analysis of current crowding and IR drops in face-to-face 3D IC TSVs. For current density and voltage predictions, the proposed framework attains R² scores of 0.9776 and 0.9952 compared to ground truth results, respectively. Our framework also demonstrates over 837× speedup than ANSYS Q3D Extractor. Furthermore, the proposed framework outperforms other machine learning-based (ML-based) methods, including the state-of-the-art method.