Research on PCB Module Automatic Layout Algorithm based on Deep Reinforcement Learning

With the rapid development of global electronic design automation (EDA) technology and the intensification of technological competition between China and the United States, the research on automatic layout algorithms for PCB (printed circuit board) modules has become particularly important. This article explores a new PCB module automatic layout algorithm based on deep reinforcement learning methods.Firstly, the challenges faced by PCB design and the limitations of existing algorithms were introduced. Then, two different state encoding methods were proposed: direct feature embedding and graph structure feature based. In the direct feature embedding method, the effective representation of the layout environment state is achieved by encoding the component feature information into different channels of the state matrix, as well as the layout area labeling method based on pin numbers. The method based on graph structure features utilizes the MLP model to extract node and edge information from the PCB circuit diagram, which is used as input for deep reinforcement learning neural networks to more comprehensively capture the complexity and connectivity of PCB layout. In addition, a deep reinforcement learning neural network structure suitable for different state encoding methods was designed, and the effectiveness and performance of the proposed algorithm were verified through experiments.The experimental results show that the PCB module automatic layout algorithm based on deep reinforcement learning has significant advantages in improving layout efficiency and optimizing design performance, providing new ideas and methods for promoting independent innovation in China’s EDA technology.