Work

An Improved YOLOv7-Tiny-Based Algorithm for Wafer Surface Defect Detection

Year: 2025

Type: article

Abstract: Wafer surface defect detection is a critical component in the chip manufacturing process. To address the shortcomings of manual inspection and the limitations of existing machine learning methods, this paper proposes a wafer defect detection algorithm based on an improved YOLOv7-tiny. First, a coordinate attention (CA) module is incorporated into the feature extraction network to enhance the network’s ability to learn features at defect locations. Next, a lightweight convolutional module, ghost shuffle convolution (GSConv), is introduced into the feature fusion network to reduce the network’s parameter count while maintaining a certain level of detection accuracy. Finally, the loss function is optimized by adopting IoU with minimum points distance (MPDIoU) to address issues such as small sizes and dense distributions. Experiments conducted on a self-constructed dataset show that the improved algorithm achieved a mean Average Precision (mAP) of 90.1%, representing a 3.2% increase over the original algorithm. The model size is only 5.85MB and the detection speed has been effectively enhanced, providing valuable insights for research in industrial real-time detection applications.

Source: IEEE Access

DOI: 10.1109/access.2025.3528242

Authors: Mengyun Li, Xueying Wang, Hongtao Zhang, Xiaofeng Hu

Institution: China Jiliang University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: National Key Research and Development Program of China, Science and Technology Program of Zhejiang Province