Routing Generative Pre-Trained Transformers for Printed Circuit Board

In recent years, the growing integration density of electronic devices has presented significant challenges for Printed Circuit Boards (PCBs), with routing emerging as a particularly intricate and labor-intensive task. In this work, we introduce a new routing method, PCB Routing GPT (PRG), leveraging Generative Pre-Trained Transformers (GPT) in the PCB domain. Our approach tokenizes routing patterns, transforming the routing process into token encoding, and facilitating database creation akin to a language model. PRG employs a fusion structure, integrating start and end position information with current flow encoding to predict flow encoding for each wire. In contrast to traditional methods relying on trial and error, PRG's pre-training phase learns from the routing of human experts, summarizing routing patterns effectively. Moreover, PRG resolves wire entanglement issues encountered in traditional routing by employing a parallel routing strategy, enhancing efficiency. Utilizing a Transformer architecture enables GPU acceleration, leading to significant improvements in routing density and speed, achieving state-of-the-art results.