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Optimized self-adapting contrast enhancement algorithm for wafer contour extraction

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Abstract

The aim is to design a self-adapting contrast enhancement algorithm that can remove the background noise and divide the foreground and background very well. For the self-adapting contrast enhancement algorithm, we looked for all background RGB pixel values, and found the minimum values in the RGB components so that ∂=255/min(Ipixel(R, G, B)). The results were compared to classical contrast enhancement techniques. Five steps to extract the wafer contour, respectively, were the preservation filter, self-adapting contrast enhancement, closing morphological operation, image binarization based on the Otsu method and using the first derivative extract contour. The results of contour extraction were compared with the traditional Canny, Sobel and Scharr algorithms using different parameter values. The experiment results showed that the self-adapting contrast enhancement algorithm could remove the background noise effectively compared to traditional methods. In comparison with the Canny, Sobel and Scharr algorithms, the first derivative after the original image filtering by optimized self-adapting contrast enhancement algorithm was better at extracting wafer contours. Therefore, we concluded that the wafer self-adapting contrast enhancement algorithm method was correct and feasible. The theories in this paper could be applied to image processing in the agricultural, industrial, and military fields.

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References

  1. Abdou IE, Pratt WK (1979) Quantitative design and evaluation of enhancement/thresholding edge detectors. Proc IEEE 67(5):753–763

    Article  Google Scholar 

  2. Abdulrahman H, Magnier B, Montesinos P (2017) From contours to ground truth: How to evaluate edge detectors by filtering

  3. Arulampalam MS, Maskell S, Gordon N et al (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188

    Article  Google Scholar 

  4. Aytekin Ç, Rezaeitabar Y, Dogru S et al (2015) Railway fastener inspection by real-time machine vision. IEEE Trans Syst Man Cybern Syst 45(7):1101–1107

    Article  Google Scholar 

  5. Bosseboeuf A, Rizzi J, Coste P, et al. (2017) Wafer bonding defects inspection by IR microphotoelasticity in reflection mode//Low Temperature Bonding for 3D Integration (LTB-3D), 2017 5th International Workshop on. IEEE, p 19–19

  6. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising//Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on IEEE 2:60–65

  7. Canny JF (1983) Finding edges and lines in images

  8. Chen GH, Wells KB, Oh SB (2017) Wafer inspection with focus volumetric method: U.S. Patent 9,816,940.

  9. Chen H, Wu K, Shifrin E, et al (2017) Array mode repeater detection: U.S. Patent 9,766,186

  10. Fredj AH, Malek J (2016) Fast oriented anisotropic diffusion filter//2016 11th International Design & Test Symposium (IDT). IEEE, p 308–312

  11. Gao L, Ray-Chaudhuri AK, Babulnath R, et al (2015) Defect detection and classification based on attributes determined from a standard reference image: U.S. Patent Application 14/612,192

  12. Gasparotto Boaventura AG (2009) Method to evaluate the performance of edge detector

  13. Hiner D, Kim DW, Ahn SG, et al (2015) Multi-die chip on wafer thermo-compression bonding using non-conductive film//Electronic Components and Technology Conference (ECTC), 2015 IEEE 65th. IEEE, p 17–21

  14. Joshi MA (2018) Digital image processing: An algorithmic approach. PHI Learning Pvt. Ltd.

  15. Karimi MH, Asemani D (2014) Surface defect detection in tiling industries using digital image processing methods: analysis and evaluation. ISA Trans 53(3):834–844

    Article  Google Scholar 

  16. Kim S, Oh I (2017) Automatic defect detection from SEM images of wafers using component tree. Journal of Semiconductor Technology and Science 17(1):86–93

    Article  Google Scholar 

  17. Koch C, Georgieva K, Kasireddy V et al (2015) A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv Eng Inform 29(2):196–210

    Article  Google Scholar 

  18. Kwon OS, Park CS, Lim CR (2014) A defect management system for reinforced concrete work utilizing BIM, image-matching and augmented reality. Autom Constr 46:74–81

    Article  Google Scholar 

  19. Kwon BK, Won JS, Kang DJ (2015) Fast defect detection for various types of surfaces using random forest with VOV features. Int J Precis Eng Manuf 16(5):965–970

    Article  Google Scholar 

  20. Kyeremateng NA, Brousse T, Pech D (2017) Microsupercapacitors as miniaturized energy-storage components for on-chip electronics. Nat Nanotechnol 12(1):7–7

    Article  Google Scholar 

  21. Lakcher A, Le-Gratiet B, Ducoté J, et al (2017) Robust 2D patterns process variability assessment using CD-SEM contour extraction offline metrology//Metrology, Inspection, and Process Control for Microlithography XXXI. International Society for Optics and Photonics, 10145:1014514

  22. Lee C, Gao L, Luo T, et al (2017) Selecting one or more parameters for inspection of a wafer: U.S. Patent 9,601,393

  23. Liu Y, Nie L, Han L, et al (2015) Action2Activity: recognizing complex activities from sensor data//IJCAI. 2015, p 1617–1623

  24. Liu X, Cheung G, Wu X (2015) Joint denoising and contrast enhancement of images using graph Laplacian operator//AcOust ics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. IEEE, p 2274–2278

  25. Liu Y, Nie L, Liu L et al (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  26. Liu D, Francis D, Faili F et al (2017) Impact of diamond seeding on the microstructural properties and thermal stability of GaN-on-diamond wafers for high-power electronic devices. Scr Mater 128:57–60

    Article  Google Scholar 

  27. Liu MY, Breuel T, Kautz J (2017) Unsupervised image-to-image translation networks//Advances in Neural Information Processing Systems. p 700–708

  28. Malloy M, Thiel B, Bunday BD, et al (2015) Massively parallel E-beam inspection: enabling next-generation patterned defect inspection for wafer and mask manufacturing//Alternative Lithographic Technologies VII. International Society for Optics and Photonics, 9423:942319

  29. Ren R, Hung T, Tan KC (2018) A generic deep-learning-based approach for automated surface inspection. IEEE Transactions on Cybernetics 48(3):929–940

  30. Sadykova D, James AP (2017) Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review//2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, p 2366–2369

  31. Selvi SST, Nasira GM (2017) An effective automatic fabric defect detection system using digital image processing. J Environ Nanotechnol 6(1):79–85

  32. Shifrin E, Kulkarni A, Bhaskar K, et al (2016) Detecting defects on a wafer: U.S. Patent 9,355,208

  33. Singh K, Kapoor R (2014) Image enhancement using exposure based sub image histogram equalization . Pattern Recogn Lett 36:10–14

    Article  Google Scholar 

  34. Sobel I, Feldman G (1968) A 3x3 isotropic gradient operator for image processing. A talk at the Stanford Artificial Project in, p 271–272.

  35. Sonka M, Hlavac V, Boyle R (2014) Image processing, analysis, and machine vision. Cengage Learning

  36. Van Dokkum PG (2001) Cosmic-ray rejection by Laplacian edge detection. Publ Astron Soc Pac 113(789):1420

    Article  Google Scholar 

  37. Vincent O R, Folorunso O (2009) A descriptive algorithm for sobel image edge detection //Proceedings of Informing Science & IT Education Conference (InSITE). California: Informing Science Institute 40:97–107

  38. Wang D, Haese-Coat V, Ronsin J (1995) Shape decomposition and representation using a recursive morphological operation. Pattern Recogn 28(11):1783–1792

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Acknowledgments

This work was supported by the National Key R&D Program of China (No.2017YFB1301203), National Natural Science Foundation of China (No.51775492) and Robotics Institute of Zhejiang University under Grant K11808.

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  1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China

    Zhiyong Yu, Jin Wang & Guodong Lu

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  1. Zhiyong Yu
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  2. Jin Wang
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  3. Guodong Lu
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Correspondence to Jin Wang.

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Yu, Z., Wang, J. & Lu, G. Optimized self-adapting contrast enhancement algorithm for wafer contour extraction. Multimed Tools Appl 78, 32087–32108 (2019). https://doi.org/10.1007/s11042-019-08019-9

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  • DOI: https://doi.org/10.1007/s11042-019-08019-9

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