D³PointNet: Dual-Level Defect Detection PointNet for Solder Paste Printer in Surface Mount Technology

Abstract

In the field of surface mount technology (SMT), early detection of defects in production machines is crucial to prevent yield reduction. In order to detect defects in the production machine without attaching additional costly sensors, attempts have been made to classify defects in solder paste printers using defective solder paste pattern (DSPP) images automatically obtained through solder paste inspection (SPI). However, since the DSPP images are sparse, have various sizes, and are hardly collected, existing CNNbased classifiers tend to fail to generalize and over-fitted to the train set. Besides, existing studies employing only multi-label classifiers are less helpful since when two or more defects are observed in the DSPP image, the location of each defect can not be specified. To solve these problems, we propose a dual-level defect detection PointNet (D 3 PointNet), which extracts point cloud features from DSPP images and then performs the defect detection in two semantic levels: a micro-level and a macro-level. In the micro-level, a type of printer defect per point is identified through segmentation. In the macro-level, all types of printer defects appearing in a DSPP image are identified by multi-label classification. Experimental results show that the proposed D 3 PointNet is robust to the sparsity and size changes of the DSPP image, and its exact match score was 10.2% higher than that of the existing CNN-based state-of-the-art multi-label classification model in the DSPP image dataset.

Investigation on SMT Product Defect Recognition Based on Multi-Source and Multi-Dimensional Data Reconstruction

“…The parameters were initialized with a weight matrix that had a mean of 0.5 and a standard deviation of 0.5. The traditional machine learning model support vector machine (SVM) [18] was introduced in order to demonstrate the advantages of the CNN in solving multi-classification problems.…”

“…The results showed that the proposed method is superior to the neural network classification method alone in terms of the accuracy of its classification. The authors of [18] proposed a dual-level defect detection algorithm, called PointNet, that extracts point cloud features from defective solder paste pattern images automatically obtained through solder paste inspection and performed defect detection at the micro-level and the macro-level for the case where two or more defects can be observed in the image. The experimental results showed that the proposed D3PointNet algorithm is robust to changes in the sparsity and size of the DSPP image, and its exact match score was 10.2% higher than that of the state-of-the-art CNN-based multi-label classification model on the DSPP image dataset.…”

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

“…Furthermore, point cloud alignment methods based on ICP [13] and Octree [14] have been applied to defect detection by model comparison. When a large amount of training data is available, defect detection models can be trained using SVM [23], 3D convolutional neural networks [8,24,25], and other methods. However, the model comparison-based method suffers from the low efficiency of 3D modelling and the timeconsuming detection process [26].…”

Few-Shot PCB Surface Defect Detection Based on Feature Enhancement and Multi-Scale Fusion

“…PCB surface defect detection has always been a challenging task, and the detection method based on deep learning has great development potential. In view of the different sizes of the defective PCB solder paste, Park et al [23] improved the traditional convolution neural network. They proposed a double-layer defect detection point network which could detect defects at both the micro and macro semantic levels.…”