Insulated Gate Bipolar Transistor Solder Layer Defect Detection Research Based on Improved YOLOv5

The expanding market scale of the insulated gate bipolar transistor as a new type of power semiconductor device has higher insulated gate bipolar transistor soldering requirements. However, there are some small bubbles difficult to detect. The accuracy and speed of existing detection algorithms are difficult to meet the requirements of automated quality monitoring. For solving these problems, a detection data set of solder layer images captured by X-ray and labeled was made and an improved algorithm based on YOLOv5 was proposed, which can detect defects accurately and at a fast speed. The main contributions of this research are as follows: (1) a tiny bubble detection layer that further integrates the deep feature information and shallow feature information is added to improve the model's ability to detect small bubbles; (2) to speed up model convergence by optimizing anchor frame parameters; (3) we change the EIoU loss function as the bounding box loss function to solve the sample imbalance of the dataset; (4) combine the Swin Transformer structure to improve the convolution module and form a new feature extraction module, and introduce it into the backbone layer to improve the detection accuracy. The results of the experiment show that the overall performance of the improved network is better than the original and mainstream detection algorithms. The accuracy of the improved YOLOv5_SEST has reached 94.5% and 5.6% improvement in mAP for common bubble defect detection compared to the original algorithm. Our model size is only 5.3 MB, and the detection speed reaches 110 f/s. Therefore, the improved YOLOv5_SEST can well meet the requirements of automated quality monitoring of insulated gate bipolar transistors.