Wafer defects in semiconductor manufacturing can directly damage the physical structure and circuit integrity of wafers, leading to the functional failure of chips. To address this problem, this paper proposes a Dual-Axis Attention-enhanced multi-scale fusion You Only Look Once version 13 (DAS-YOLOv13) model. Based on YOLOv13n, the model is specifically designed for the fast and accurate detection of tiny, multi-scale defects on wafer surfaces. It integrates innovative components such as a dual-axis attention module, an adaptive dynamic multi-scale representation module, and a self-modulation feature aggregation module. By enhancing salient feature expression, improving cross-scale representation capability, and optimizing deep semantic fusion strategies, the model achieves effective defect detection. On the wafer defect dataset, the DAS-YOLOv13 model achieves a mean Average Precision (mAP) of 74.2%, which is 4.3% higher than that of YOLOv13n; the Average Precision at an Intersection over Union (IoU) threshold of 50% (mAP50) reaches 92.9%. The results demonstrate that DAS-YOLOv13 effectively improves the detection accuracy of tiny, multi-scale defects through structural optimization. It provides a reliable solution for high-precision wafer detection in semiconductor manufacturing and can be seamlessly integrated into high-precision semiconductor automated inspection scenarios.
Zhang et al. (Mon,) studied this question.