Automatic detection of wire bonding defects in microwave components using multi-stage hybrid methods based on deep learning

Abstract Wire bonding is one of the main processes in micro-assembly, as its quality directly affects the reliability of microwave components and their operating characteristics. Therefore, it is important to detect defects in wire bonding. Due to the diversity of chips, connections, and circuit substrates, the wire bonding regions vary greatly. Using image processing methods exclusively requires expert knowledge, and the solution lacks versatility. Meanwhile, in highly complex industrial scenarios, relying on end-to-end deep learning method alone cannot accomplish the task constrained by data volume and task difficulty. Therefore, we propose a three-stage wire bonding defect detection method that integrates deep learning with traditional image processing methods for the detection of complex wire bonding defects. In order to address the defect detection of more types of complex bonding images, we divide them into four categories and complete the detection step by step. In the first two stages, semantic segmentation and image processing methods are used in turn to complete the extraction of the region of interest, and in the third stage, we propose a defect recognition model based on Siamese network with a new feature fusion structure to enhance feature learning. Experiments show that the proposed three-stage method, which combines deep learning and image processing, can effectively detect wire bonding defects and is suitable for handling highly complex engineering tasks with greater efficiency and intelligence.