Based Routability

Based routability research focuses on predicting and improving the ease with which electronic circuits or pedestrian pathways can be routed, aiming to optimize design efficiency and functionality. Current research employs graph neural networks and U-Net variants, often incorporating inception modules, to predict routability from various data sources, including aerial imagery and circuit netlists, with a growing emphasis on federated learning to address data privacy concerns. These advancements have significant implications for improving chip design automation, reducing manufacturing costs, and enhancing the usability of pedestrian navigation applications in urban planning.