mlsanityData to 14 Oct 2026

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

arXiv 2506.10577 ↗First posted 12 Jun 2025cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 12 Jun 2025.

Builds on

19 of 31 references.

References · 19

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7 more
  1. 2020Chip Placement with Deep Reinforcement Learningcited by 83
  2. 2021Machine Learning for Electronic Design Automation: A Surveycited by 72
  3. 2023CktGNN: Circuit Graph Neural Network for Electronic Design Automationcited by 58
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026ALINC: Active Learning for Inductive Node Classification via Graph Samplingcited by 0