Enhancing Wafer Notch Detection for Ion Implantation: Optimized YOLOv8 Approach with Global Attention Mechanism
Source abstract
Abstract
In the semiconductor manufacturing process, precise control of wafer notch angles during ion implantation is critical to prevent channeling effects that can lead to defects. Current detection methods face challenges in identifying wafer notches accurately, particularly under varying conditions. This paper proposes an enhanced YOLOv8 model tailored for small object detection, specifically aimed at improving the accuracy of wafer notch angle detection. By addressing class imbalance issues, introducing a small target detection layer and two new detection heads, and optimizing the global attention mechanism within the model’s backbone, we significantly improve detection performance. Experimental results demonstrate that our improved YOLOv8 model achieves a mean average precision of 93.4%, outperforming existing YOLO versions and other relevant models. This study not only enhances the reliability of wafer notch detection but also offers insights into optimizing object detection algorithms for precision manufacturing applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Thin-Film Transistor Technologies · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Convergence Engineering, Hanyang University, Seoul 04763, Republic of Korea
Funding
Korea Institute of Energy Technology and Evaluation Planning (KETEP)
References (26)
Show source-provided references
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