<ul data-eligibleForWebStory="true"><li>The optimization of Printed Circuit Board (PCB) schematics is essential for high-quality electronic devices by adding components like pull-up resistors or decoupling capacitors.</li><li>Manual optimizations are time-consuming and often neglected due to a shortage of skilled engineers, leading to higher costs for troubleshooting and increased electronic waste.</li><li>An automated approach using Graph Neural Networks (GNNs) for adding components to PCB schematics is introduced in this study to improve circuit robustness and reliability.</li><li>The approach represents PCB schematics as bipartite graphs and employs a node pair prediction model based on GNNs.</li><li>The research applies this method to three crucial PCB design optimization tasks and evaluates various GNN architectures' performance using real-world datasets labeled by experts.</li><li>The study demonstrates that GNNs can effectively address these optimization tasks with high accuracy, potentially automating PCB design optimizations in a time- and cost-efficient manner.</li></ul>

Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics

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