An Auto Chip Package Surface Defect Detection Based on Deep Learning

Defect detection in chip packaging is a crucial step to ensure product quality and reliability. Traditional methods typically employ image-processing techniques for defect detection during the chip manufacturing process. However, these solutions require manual feature extraction and have limited adaptability to complex scenarios. Thus, deep-learning (DL)-based methods have received widespread attention. Nevertheless, they may fail to achieve the requirements of real-time and high accuracy, and effective datasets are still missing. In this article, we construct a new chip package surface defect detection dataset, which contains 2919 images and four common defect types. To our knowledge, it is the only dataset for simultaneous detection of multiple chips. Also, we propose a real-time chip package surface defect detection method based on the you only look once version 7 (YOLOv7) model to solve the challenge of detecting small targets. In particular, we utilize k -means++ to recluster the anchor frames, merge the convolutional block attention module (CBAM) attention mechanism and receptive field block (RFB) structure, as well as replace traditional nonmaximum suppression (NMS) with our newly proposed confidence propagation cluster (CP-Cluster) to further increase detection accuracy and result confidence. Finally, we evaluate our method by performing many ablation experiments on the dataset we created. The experimental results demonstrate that compared to the original YOLOv7, the proposed method improves the mean average precision@0.5 (mAP@0.5) by 1.39%, the speed of detection by 21.6%, reduces the amount of computation by 17.7%, and the number of parameters by 66.4%, respectively. This proves the superiority and practicality of the proposed method.