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

2025-06-12

AI Summary

To address time-consuming manual optimization of PCB schematics and reliability defects arising from engineering talent shortages, this paper proposes the first end-to-end automated optimization method based on bipartite graph modeling and graph neural networks (GNNs), capable of accurately identifying locations and inserting critical enhancement components—such as pull-up resistors and decoupling capacitors. Innovatively, schematics are modeled as device–pin bipartite graphs, and a node-pair relation prediction framework is designed to enable topology-agnostic generalization. Evaluated on a real-world dataset annotated by domain experts, the method achieves high accuracy across three core optimization tasks. It significantly reduces design cycle time, lowers post-layout debugging costs, and minimizes electronic waste generation. Experimental results demonstrate its effectiveness and practicality in industrial PCB design workflows.

Technology Category

• Machine Learning: Optimization
• Constraint Satisfaction and Optimization: Satisfiability
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• Automating PCB optimization by adding components using GNNs
• Addressing shortage of engineers for manual PCB design improvements
• Reducing electronic waste through efficient PCB schematic optimization

Innovation

• Uses Graph Neural Networks for PCB optimization
• Automates component addition via node prediction
• Compares GNN architectures on real-world datasets