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Optimization and Validation of Wafer Surface Defect Detection Algorithm Based on RT-DETR
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DOI：10.1109/ACCESS.2025.3543525.png)
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Abstract
En 中文
In response to the issue of poor detection performance on wafer surface defect spots and elongated scratches, an improved RT-DETR method for wafer surface defect detection is proposed. Firstly, a dynamic snake convolutional layer is introduced to detect elongated scratches where conventional convolutional kernels fail to extract features effectively. Secondly, to address the problem of information loss in small targets, an attention-based Transformer encoder module and a feature fusion network based on residual thinking are proposed. Finally, verification is conducted using a wafer test dataset. Experimental results demonstrate that compared to the original RT-DETR method, the model exhibits a 4.1% improvement in detecting small particles and a 5.4% improvement in scratch detection performance. Fully meeting the requirements of intelligent manufacturing and high detection accuracy.
Keywords:
Feature extraction
Kernel
Defect detection
Shape
Computational modeling
Semiconductor device modeling
Data mining
Accuracy
Transformers
Standards
Defects detection
deep learning
object detection
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W