Deep-Learning Methods for Defect Inspection of Plated Through Holes With Clustering-Based Auto-Labeling and GAN-Based Model Training

Abstract

This paper presents the integration of several deep learning techniques for defect inspection of plated through-hole (PTH) on printed circuit boards (PCBs). In our proposed system, the object detection technology of You Only Look Once (YOLO) allocates the position of PTHs; a semi-automatic clustering mechanism distinguishes normal and defective PTHs for collecting training data. A convolution neural network based on the ResNet framework is established for detecting PTH defects. A retrain mechanism is designed for retracting misclassified PTHs and updating the recognition model. The generative adversarial network (GAN) is employed to deal with the insufficiency of negative training samples. The detection model can achieve 98.96% accuracy with continuous retraining and data augmentation. Further, a filtering mechanism based on template analysis eliminates ambiguous outputs of GAN and improves the accuracy of the detection model. This study integrates these deep learning techniques to establish a PTH defect detection system tested in a PCB manufactory against real PTHs and gained an overall prediction accuracy of 99.48%.

Keywords

• Automated optical inspection (AOI)
• defect detection
• deep learning
• electroplated through-hole (PTH)
• printed circuit board (PCB)
• generative adversarial network (GAN)