Automated detection of defects in Printed Circuit Board (PCB) manufacturing
using Solder Paste Inspection (SPI) and Automated Optical Inspection (AOI)
machines can help improve operational efficiency and significantly reduce the
need for manual intervention. In this paper, using SPI-extracted features of 6
million pins, we demonstrate a data-centric approach to train Machine Learning
(ML) models to detect PCB defects at three stages of PCB manufacturing. The 6
million PCB pins correspond to 2 million components that belong to 15,387 PCBs.
Using a base extreme gradient boosting (XGBoost) ML model, we iterate on the
data pre-processing step to improve detection performance. Combining pin-level
SPI features using component and PCB IDs, we developed training instances also
at the component and PCB level. This allows the ML model to capture any
inter-pin, inter-component, or spatial effects that may not be apparent at the
pin level. Models are trained at the pin, component, and PCB levels, and the
detection results from the different models are combined to identify defective
components.

使用印刷电路板制造中的焊膏检测与自动光学检测机器自动检测缺陷可以提高运营效率并显著减少人工干预的需求。本文使用提取的 600 万个引脚的焊膏检测特征，展示了一种基于数据的方法来训练机器学习模型，以便在印刷电路板制造的三个阶段检测缺陷。这些 600 万个 PCB 引脚对应着 15387 个 PCB 中的 200 万个元件。我们使用基于梯度提升的机器学习模型迭代数据预处理步骤以提高检测性能。通过使用元件和 PCB ID 的引脚级焊膏检测特征，我们还在元件和 PCB 级别上开发了培训实例。这使得机器学习模型能够捕捉到可能在引脚级别不明显的引脚间、元件间或空间效应。我们在引脚、元件和 PCB 级别上训练模型，并将来自不同模型的检测结果组合起来识别有缺陷的元件。