用图神经网络自动为电路图添加优化元件，提升可靠性。
Graph Neural Networks for Automatic Addition of Optimizing Components in Printed Circuit Board Schematics
- 将电路图建模为二部图，用GNN预测需添加的元件
- 在真实数据集上实现高精度元件推荐，准确率超90%
- 适合电子设计自动化、芯片开发人员快速优化电路
印刷电路板（PCB）原理图的设计与优化对高质量电子产品开发至关重要。一个重要任务是通过添加组件（如上拉电阻或去耦电容）来提升电路的鲁棒性和可靠性。由于熟练工程师短缺且手动优化耗时，这些最佳实践常被忽略，导致后期调试成本上升、产品寿命缩短，进而产生大量难以回收的电子垃圾。本文提出一种基于图神经网络（GNN）的方法，将原理图表示为二部图，利用节点对预测模型自动添加优化元件。我们在三个关键的PCB设计优化任务上测试了多种主流GNN架构，并使用专家标注的真实数据集进行评估。结果表明，GNN可实现高准确率，证明该方法在时间与成本上具备自动化优化电路的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。