GPCB Routing: Generative Pretrained Transformers-Based Printed Circuit Board Routing Method

As electronic devices become increasingly compact, designing printed circuit boards (PCBs) has become more challenging, particularly in the routing step, which is now more complex and time-consuming. In this work, we propose a method that applies generative pretrained transformers (GPTs) for PCB routing, referred to as GPCB routing. Initially, we convert the detailed routing information of the PCB into network flow-based encodings. Consequently, GPCB routing tokenizes routing patterns, effectively transforming the routing task into a form of token encoding prediction. To enhance prediction accuracy, we implement a 2-D sliding window with a local memory scheme, thereby expanding the sensing area of GPCB. Additionally, we propose a multi-information fusion scheme to identify the start and end points of multiple wires to further improve the prediction accuracy. Compared to existing routing methods, GPCB has the distinct advantage of learning routing strategies from human experts, breaking the limitations of traditional model-based routing approaches. Moreover, GPCB operates as a parallel routing method capable of predicting multiple routes simultaneously, resulting in significant enhancements in routing performance. Based on the experimental results, GPCB consistently outperforms in terms of routability, runtime, and wirelength.