A deep reinforcement learning framework for optimized dummy pad placement in PCB electroplating

The placement of dummy pads is critical in large-scale printed circuit board (PCB) design to ensure uniform copper plating. However, space constraints make dense arrangement difficult in blank areas. Thus, an efficient layout strategy is urgently needed to achieve optimal plating uniformity with limited dummy pads. Existing algorithms struggle with large search spaces, high evaluation costs, and limited accuracy due to the complexity and scale of full-board PCB optimization. To address these challenges, we propose a model framework that integrates neural network prediction with reinforcement learning optimization. The PCB is first partitioned into smaller sub-regions to reduce computational complexity. A reward network is then constructed, incorporating spatial pyramid pooling and external attention mechanisms to capture both local and global spatial features. This network enables accurate prediction of plating outcomes under different dummy pad configurations. Guided by these predictions, we employ the proximal policy optimization (PPO) algorithm to train a dummy pad layout network that autonomously explores optimal design strategies. Importantly, a real-time reward function is introduced to decompose the contribution of each dummy pad, effectively mitigating the sparse reward problem and accelerating convergence. Experimental results demonstrate that our approach outperforms traditional engineering methods in terms of copper thickness uniformity and layout efficiency. Furthermore, it reveals hidden design patterns and underlying physical principles, providing both practical guidance and theoretical insight into PCB layout optimization.