Optimized YOLOv8 based on SGW for surface defect detection of silicon wafer

Wafer detection is a critical procedure in IC manufacturing. Deep learning models are extensively used to identify the wafer defects. The key issue is how to recognize the small defects in complex backgrounds reducing the probability of misjudge and enhancing the detection accuracy. To solve the problems, we proposed a new detection model, SGW-YOLOv8 ( SimAM-GhostConv-Wise IOU), based on the YOLOv8 model. The SimAM attention mechanism was added to enhance the network's focus on important positions without increase of the parameter numbers. The GhostConv improved the backbone network, and the Wise-IOU ( Weighted Intersection over Union) loss function was introduced to address the deviation of evaluation results caused by the traditional loss function. Moreover, an additional detection head was appended to YOLOv8 to improve the model's capability of recognizing small targets. The dataset containing six types of defects was established by generating 6000 images of silicon wafers. The experimental results demonstrated that the mAP@0.5 of SGWYOLOv8 increased by 4.8% compared to YOLOv8, and the model parameters are decreased by 11.8%. Therefore, the proposed SGW-YOLOv8 model is potential for wafer detection in IC industry.