Have a personal or library account? Click to login
CC-De-YOLO: A Multiscale Object Detection Method for Wafer Surface Defect Cover

CC-De-YOLO: A Multiscale Object Detection Method for Wafer Surface Defect

By: Jianhong Ma,  Tao Zhang,  Xiaoyan Ma and  Hui Tian  
Open Access
|Sep 2024

References

  1. Chen S. H., Kang C. H., Perng D. B.: Detecting and measuring defects in wafer die using gan and yolov3. Applied Sciences, 10, 23, 2020, 8725.
  2. Ding, X.H., Zhang, X.Y., Man, N.N., Han, J.G., Ding, G.G., Sun, J: RepVGG: Making VGG-style ConvNets great again. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021;
  3. Elfwing S., Uchibe E., Doya K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks, 107, 2018, 3-11.
  4. Ge Z., Liu S., Wang F., et al.: Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430, 2021.
  5. Han H., Gao C., Zhao Y., et al.: Polycrystalline silicon wafer defect segmentation based on deep convolutional neural networks. Pattern Recognition Letters, 130, 2020, 234-241.
  6. He K., Gkioxari G., Dollár P., et al.: Mask r-cnn, Proceedings of the IEEE international conference on computer vision. 2017: 2961-2969.
  7. Ho C. M., Tai Y. C.: Micro-electro-mechanical-systems (MEMS) and fluid flows. Annual review of fluid mechanics, 30, 1, 1998, 579-612.
  8. Hou Q., Zhou D., Feng J.: Coordinate attention for efficient mobile network design, Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, 13713-13722.
  9. Hu J., Shen L., Sun G.: Squeeze-and-excitation networks, Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, 7132-7141.
  10. Jin C. H., Na H. J., Piao M., et al.: A novel DBSCAN-based defect pattern detection and classification framework for wafer bin map. IEEE Transactions on Semiconductor Manufacturing, 32, 3, 2019, 286-292.
  11. Kim Y., Cho D., Lee J. H.: Wafer map classifier using deep learning for detecting outof-distribution failure patterns, 2020 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA). IEEE, 2020, 1-5.
  12. Kong Y., Ni D.: A semi-supervised and incremental modeling framework for wafer map classification. IEEE Transactions on Semiconductor Manufacturing, 33, 2020, 62-71.
  13. Li C., Li L., Jiang H., et al.: YOLOv6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976, 2022.
  14. Liu S., Qi L., Qin H., et al.: Path aggregation network for instance segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, 8759-8768.
  15. Liu W., Anguelov D., Erhan D., et al.: Ssd: Single shot multibox detector, Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14.
  16. Lou A., Loew M.: Cfpnet: channel-wise feature pyramid for real-time semantic segmentation, 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021, 1894-1898.
  17. Miyajima H., Mehregany M.: High-aspect-ratio photolithography for MEMS applications. Journal of microelectromechanical systems, 4, 4, 1995, 220-229.
  18. Ren S., He K., Girshick R., et al.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 2015, 28.
  19. Selvaraju R. R., Cogswell M., Das A., et al.: Grad-cam: Visual explanations from deep networks via gradient-based localiza tion, Proceedings of the IEEE international conference on computer vision. 2017, 618-626.
  20. Shinde P. P., Pai P. P,: Adiga S P. Wafer defect localization and classification using deep learning techniques. IEEE Access, 10, 2022, 39969-39974.
  21. Shorten C., Khoshgoftaar T. M.: A survey on image data augmentation for deep learning. Journal of big data, 6, 1, 2019, 1-48.
  22. Wang C. Y., Bochkovskiy A., Liao H. Y. M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023, 7464-7475.
  23. Wang J., Chen K., Xu R., et al.: Carafe: Content-aware reassembly of features, Proceedings of the IEEE/CVF international conference on computer vision. 2019, 3007-3016.
  24. Wang J., Xu C., Yang Z., et al.: Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition. IEEE Transactions on Semiconductor Manufacturing, 33, 4, 2020, 587-596.
  25. Woo S., Park J., Lee J. Y., et al.: Cbam: Convolutional block attention module, Proceedings of the European conference on computer vision (ECCV). 2018, 3-19.
  26. Xie L., Huang R., Gu N., Cao Z.: A novel defect detection and identification method in optical inspection, Neural Computing and Applications, 2013, 1–10.
  27. Xifeng L.: Image registration-based wafer surface defect detection (Chinese). Instrumentation and analytical monitoring,2020, 1-4
DOI: https://doi.org/10.2478/fcds-2024-0014 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Language: English
Page range: 261 - 285
Submitted on: Sep 17, 2023
Accepted on: May 19, 2024
Published on: Sep 19, 2024
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Jianhong Ma, Tao Zhang, Xiaoyan Ma, Hui Tian, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.