BallGridArray Chip Defects Detection and Classification Using Patchbased Modified YOLOv3 The tiny size of defects and the noise information in the BallGridArray (BGA) Chip images have been challenging the imageprocessing based visual inspection systems in the Integrated Circuit (IC) manufacturing industry. Moreover, the gradient vanishing and high timeconsuming problems of deep neural network models are a big obstacle for its application to solve industrial projects. This paper focuses on proposing a modified version of the YOLOv3 model, a leading object detection and classification method in terms of speed; and its application to deal with the problem of detecting and classifying defects on BGA Chip Images. There are five modifications constructed on (4) YOLOv3 architecture that are aimed to enhance the ability of feature extraction towards small objects, strengthen the flow of information inside the network and eliminate the problem of redundant information. With the application of this model into BGA Chip Defects estimation, 49 patches (320x320 pixels), extracted from a single highresolution BGA Chip image (1, 450x1,450 pixels), are continuously fed into the modified YOLOv3 model to detect and classify the inner defects. As a result, the problem of BGA Chip Defects estimation is solved with the highest performance achieves an average precision of 86% at IoU (Intersection over Union) of 0.75 and an average recall of 99%.

A Hierarchical Feature Fusionbased Method for Defect Recognition with a Small Sample As one of the breakthroughs in modern manufacturing, deep learning (DL) performs largescale network architectures and achieves some outstanding performances in visionbased defect recognition. However, most of these largescale networks require a large sample for training, and a small sample might cause the networks overfitting and collapse. Since the defect often occurs with a low probability, it is costly to collect largescale samples. To overcome this problem, a hierarchical feature fusionbased method is introduced for defect recognition with a small sample. The proposed method divides a pretrained VGG16 network into different blocks, and learns the hierarchical features from the low and high level blocks. The results are better than the other methods. This result manifests the proposed method suits problem, and the defect recognition could be deployed earlier with the proposed method.