Wafer Defect Detection Based on YOLO-BA
ID：167
Submission ID：161 View Protection：ATTENDEE
Updated Time：2024-10-23 10:02:36 Hits：63
Poster Presentation
Start Time：Pending （Asia/Shanghai）
Duration：Pending
Session：[No Session] » [No Session Block]
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Abstract
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
Keywords
Wafer,Defect Detection,Deep Learning,YOLOv10,self-attention mechanism
Speaker
LuXiangning
Prof. Jiangsu Normal UniversitySubmission Author
All comments
15th August 2024 31st August 2024- Manuscript Submission
15th September 2024 - Acceptance Notification
1st October 2024 - Camera Ready Submission
1st October 2024 – Early Bird Registration
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