PCBRouteNet: A Dynamic Quadrilateral Network Flow Model-based Dataset Generation Tool for ML PCB Routing

As the complexity and density of electronic components continue to grow, manual printed circuit board (PCB) routing has become an increasingly labor intensive and costly task. However, the lack of large publicly available datasets for training machine learning (ML) models has limited potential advancements in this area. To address this gap, we introduce PCBRouteNet, a comprehensive large-scale dataset specifically designed to accelerate ML innovations in automated PCB routing. To manage the high complexity of PCB data and improve extraction efficiency, we propose a dynamic, adjustable quadrilateral network flow model to construct a network flow graph composed of quadrilateral tiles. This method efficiently converts original design data into a network flow-based format, facilitating feature extraction for both global and detailed routing. Our dataset includes over 1,000 samples of real-world PCB designs, featuring diverse layouts with varying complexities, such as multi-layer boards and high-density interconnects. Additionally, we propose several practical ML tasks that utilize PCBRouteNet to showcase its potential in advancing the efficiency and effectiveness of automated PCB routing solutions.