报告详情
Wafer Defect Detection Based on YOLO-BA
编号：167
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更新：2024-10-23 10:02:36 浏览：202次
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摘要
Abstract: With the advancement of the chip industry and manufacturing processes are becoming increasingly sophisticated, the scale and complexity of integrated circuits have significantly increased. This has made wafer manufacturing processes more intricate, leading to a higher probability and variety of wafer defects. To enhance production yield and improve process control, it is crucial to identify defects and address corresponding process issues. This paper presents an improved YOLO-BA wafer detection algorithm based on YOLOv10n. The approach introduces Bottleneck Transformers (BoT) and Adaptive Kernel Convolution (AKConv) into the backbone network, effectively suppressing interference from complex background information while reducing feature redundancy in spatial and channel dimensions. This enables more efficient feature extraction of wafer defects while maintaining model lightweightness. Experimental results show that the improved algorithm achieves a 0.9% higher recall rate, 0.8% higher mAP0.5_0.95, and 0.4% higher mAP0.5 compared to the latest YOLOv10n model, with similar parameter counts and GFLOPs.
Keywords: Wafer, Defect Detection, Deep Learning, YOLOv10, self-attention mechanism
Keywords: Wafer, Defect Detection, Deep Learning, YOLOv10, self-attention mechanism
关键词
Wafer,Defect Detection,Deep Learning,YOLOv10,self-attention mechanism
报告人
LuXiangning
Prof. Jiangsu Normal University稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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