Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics
This addresses the shortage of skilled engineers and time-consuming manual optimizations in PCB design, which often lead to higher troubleshooting costs and increased electronic waste.
The paper tackles the problem of automating the addition of optimizing components like pull-up resistors or decoupling capacitors to PCB schematics to improve robustness and reliability, showing that Graph Neural Networks (GNNs) can solve these tasks with high accuracy in a time- and cost-efficient manner.
The design and optimization of Printed Circuit Board (PCB) schematics is crucial for the development of high-quality electronic devices. Thereby, an important task is to optimize drafts by adding components that improve the robustness and reliability of the circuit, e.g., pull-up resistors or decoupling capacitors. Since there is a shortage of skilled engineers and manual optimizations are very time-consuming, these best practices are often neglected. However, this typically leads to higher costs for troubleshooting in later development stages as well as shortened product life cycles, resulting in an increased amount of electronic waste that is difficult to recycle. Here, we present an approach for automating the addition of new components into PCB schematics by representing them as bipartite graphs and utilizing a node pair prediction model based on Graph Neural Networks (GNNs). We apply our approach to three highly relevant PCB design optimization tasks and compare the performance of several popular GNN architectures on real-world datasets labeled by human experts. We show that GNNs can solve these problems with high accuracy and demonstrate that our approach offers the potential to automate PCB design optimizations in a time- and cost-efficient manner.