Fast wafer detection algorithm based on YOLOv5s

Xia Yu; Ziyou Zhang; Dong Wei

doi:10.1117/12.3035468

19 July 2024 Fast wafer detection algorithm based on YOLOv5s

Xia Yu, Ziyou Zhang, Dong Wei

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132U (2024) https://doi.org/10.1117/12.3035468
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

In order to solve the problem that traditional wafer localization methods are easily affected by light, noise, etc., and that deep learning target detection methods are slow for wafer detection, we constructed the wafer fast detection algorithm YOLOv5s-fast. firstly, in the feature extraction part, we used the FasterNet network for feature extraction, which accelerated the algorithm running speed; secondly, in order to improve the algorithm detection performance, we added BRA bi-directional routing attention (Bilevel Routing Attention) to feature fusion part of the network to strengthen the feature fusion capability. Secondly, in order to improve the detection performance of the algorithm, BRA Bilevel Routing Attention is added in the feature fusion part of the network to strengthen the feature fusion capability. Finally, the MPDIOU loss function is used to improve the model detection performance. The average detection accuracy of the algorithm is 99.50%, the number of parameters is 1064176, and the detection speed is 2.8GFLOPs, which improves both the detection speed and the detection accuracy, and at the same time realizes the algorithm lightweight.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xia Yu, Ziyou Zhang, and Dong Wei "Fast wafer detection algorithm based on YOLOv5s", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132U (19 July 2024); https://doi.org/10.1117/12.3035468

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KEYWORDS

Semiconducting wafers

Detection and tracking algorithms

Feature extraction

Feature fusion

Convolution

Education and training

Matrices

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