journal article·2024-03-05Open access

An Edge Detection Algorithm for SEM Images of Multilayer Thin Films

Wei Sun, Fang Duan, Jianpeng Zhu, Minglai Yang, Ying Wang

Coatings· Volume 14· Issue 3

Abstract

In processing multilayer thin film materials, scanning electron microscopy (SEM) is commonly employed for observation. In images of SEM, backscattered electron (BSE) images is particularly suitable for distinguishing different components and layers of the films. However, at high magnification levels, BSE images often have blurriness and noise, leading to low edge sharpness. This study proposes a method for improving the integrity and accuracy of the edges. First, we segment the image into different contrast regions using the masking algorithm. Second, we enhance the images in separate regions by the enhancement algorithm. Finally, we combine the regions by logical operations. In instantiation, we implement our approach on SEM-BSE images. It was found that the edges are significantly sharpened through the assessment of the edge evaluation algorithm.
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Research context

OpenAlex topics: Industrial Vision Systems and Defect Detection · Image Processing Techniques and Applications · Electron and X-Ray Spectroscopy Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China

SJTU-Pinghu Institute of Intelligent Optoelectronics, Hangzhou 314299, China

Center for Advanced Electronic Materials and Devices, Shanghai Jiao Tong University, Shanghai 200241, China

Funding

open Project Program of SJTU-Pinghu Institute of Intelligent Optoelectronics

References (36)

Show source-provided references
1. Sikora, M., Wojcieszak, D., Chudzyńska, A., and Zięba, A. (2023). Improved Methodology of Cross-Sectional SEM Analysis of Thin-Film Multilayers Prepared by Magnetron Sputtering. Coatings, 13.10.3390/coatings13020316 ↗
2. Pandey, P.S., and Tiwari, A.K. (2019, January 8–9). Macroparticle Detection in Surface Coating Using Image Processing. Proceedings of the 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), Sultanpur, India.10.2139/ssrn.3350263 ↗
3. Mohammed, A., and Abdullah, A. (2018, January 7–9). Scanning electron microscopy (SEM): A review. Proceedings of the 2018 International Conference on Hydraulics and Pneumatics—HERVEX, Băile Govora, Romania.
4. Measuring the randomness of micro-and nanostructure spatial distributions: Effects of scanning electron microscope image processing and analysis10.1111/jmi.13149 ↗
5. Three-dimensional integral imaging in photon-starved environments with high-sensitivity image sensors10.1364/OE.27.026355 ↗
6. An overview of state-of-the-art image restoration in electron microscopy10.1111/jmi.12716 ↗
7. Yu, L., Zhou, W., Pu, L., and Fang, W. (2020, January 24–27). SEM image quality enhancement: An unsupervised deep learning approach. Proceedings of the Metrology, Inspection, and Process Control for Microlithography XXXIV, San Jose, CA, USA.10.1117/12.2552883 ↗
8. Polarimetric image denoising on small datasets using deep transfer learning10.1016/j.optlastec.2023.109632 ↗
9. Image quality assessment: From error visibility to structural similarity10.1109/TIP.2003.819861 ↗
10. Li, J., and Weng, G. (2010, January 19–21). Analysis Image of Multiplayer Thin Films. Proceedings of the 2010 Symposium on Photonics and Optoelectronics, Chengdu, China.10.1109/SOPO.2010.5504184 ↗
11. A comprehensive review of image enhancement techniques
12. Rosenfeld, A. (1976). Digital Picture Processing, Academic Press.10.1007/3-540-07579-8 ↗
13. Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement10.1109/TCE.2008.4637632 ↗
14. An adaptive gamma correction for image enhancement10.1186/s13640-016-0138-1 ↗
15. Beyond brightening low-light images10.1007/s11263-020-01407-x ↗
16. Image enhancement through logarithmic transformation
17. Effective Contrast Enhancement techniques for Fundus images
18. Fast edge detection using structured forests
19. Image enhancement via adaptive unsharp masking10.1109/83.826787 ↗
20. Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., and Girdhar, R. (2022, January 18–24). Masked-attention mask transformer for universal image segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.00135 ↗
21. A computational approach to edge detection10.1109/TPAMI.1986.4767851 ↗
22. Methods of image edge detection: A review
23. A study of edge detection algorithms10.1016/0146-664X(82)90070-3 ↗
24. Haralick, R.M. (1987). Readings in Computer Vision, Elsevier.
25. Edge detection and linear feature extraction using a 2-D random field model10.1109/34.23115 ↗
26. A robust visual method for assessing the relative performance of edge-detection algorithms10.1109/34.643893 ↗
27. Mo, S.Q. (2011). Research on Edge Detection and Its Evaluation. [Ph.D. Thesis, Tianjin University].
28. A review of thermal absorbers and their integration methods for the combined solar photovoltaic/thermal (PV/T) modules10.1016/j.rser.2016.11.063 ↗
29. Sintering variables optimization, microstructural evolution and physical properties enhancement of nano-WC ceramics10.1016/j.jallcom.2018.07.006 ↗
30. A review on oxide/metal/oxide thin films on flexible substrates as electrodes for organic and perovskite solar cells
31. SEM analysis of grid elements in mono-crystalline and poly-crystalline based solar cell10.1016/j.matpr.2020.02.904 ↗
32. Goldstein, J.I., Newbury, D.E., Michael, J.R., Ritchie, N.W., Scott, J.H.J., and Joy, D.C. (2017). Scanning Electron Microscopy and X-ray Microanalysis, Springer.10.1007/978-1-4939-6676-9 ↗
33. Tomasi, C., and Manduchi, R. (1998, January 4–7). Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), Bombay, India.
34. Tukey, J.W. (1974, January 21–29). Mathematics and the picturing of data. Proceedings of the International Congress of Mathematicians, Vancouver, BC, Canada.
35. A review of image denoising algorithms, with a new one10.1137/040616024 ↗
36. Recent advances on image edge detection: A comprehensive review10.1016/j.neucom.2022.06.083 ↗