Yolov5-based defect detection for wafer surface micropipe

Abstract

Micropipe defects on the surface of silicon carbide wafers can have a significant impact on the quality of the wafers. Therefore, it is necessary to identify and locate them during the production process. Due to micropipe defects being small and dense, which are difficult to detect completely, we propose a real-time defect detection network model based on the Yolov5. The model adds a detection branch in the neck and head block of Yolov5 to obtain high-resolution features. To get the spatial and channel attention, we apply a CBAM attention module in each neck branch, and DA attention module in each head branch. The experiments show that our model improves AP by 1.89% and increases precision and recall by 10.12% and 2.95%, respectively, compared with the Yolov5 model. The results show that our model has a better ability to detect small and dense defects.