Xu Wei, Gu Sen, Chu Chengzhi, et al. Real-time correction of image drift in scanning electron microscope[J]. Opto-Electronic Engineering, 2018, 45(12): 180198. doi: 10.12086/oee.2018.180198
Citation: Xu Wei, Gu Sen, Chu Chengzhi, et al. Real-time correction of image drift in scanning electron microscope[J]. Opto-Electronic Engineering, 2018, 45(12): 180198. doi: 10.12086/oee.2018.180198

Real-time correction of image drift in scanning electron microscope

    Fund Project: Supported by National Natural Science Foundation of China (61774107)
More Information
  • In order to solve the problem of imaging drift in scanning electron microscope (SEM) that caused by electron beam drift, electromagnetic interference and other reasons, an image shift correction algorithm based on ORB (oriented FAST and rotated BRIEF) combing the PROSAC (progressive sample consensus) is proposed in this paper. Firstly, the ORB algorithm is used to detect the feature between the reference image and real-time image. Then the initial matching of the feature is implemented by using the Hamming distance and cross-matching. Moreover, the RANSAN (random sample consensus) optimization algorithm PROSAC is used to calculate the homography matrix between frames and the final exact homography matrix is re-iterated after eliminating exterior point. Finally, the SEM image drift is corrected in real time using the perspective transformation of the homography matrix. The experiments show that the proposed algorithm is high precision and satisfies the requirement of SEM real-time processing.
  • 加载中
  • [1] 曹耀宇, 谢飞, 张鹏达, 等.双光束超分辨激光直写纳米加工技术[J].光电工程, 2017, 44(12): 1135-1145.

    Google Scholar

    Cao Y Y, Xie F, Zhang P D, et al. Dual-beam super-resolution direct laser writing nanofabrication technology[J]. Opto-Electronic Engineering, 2017, 44(12): 1135-1145.

    Google Scholar

    [2] Gong Z, Chen B K, Sun Y, et al. Robotic Probing of Nanostructures inside Scanning Electron Microscopy[J]. IEEE Transactions on Robotics, 2014, 30(3): 758-765. doi: 10.1109/TRO.2014.2298551

    CrossRef Google Scholar

    [3] Ye X T, Zhang Y, Ru C H, et al. Automated Pick-Place of Silicon Nanowires[J]. IEEE Transactions on Automation Science and Engineering, 2013, 10(3): 554-561. doi: 10.1109/TASE.2013.2244082

    CrossRef Google Scholar

    [4] Ru C H, Zhang Y, Sun Y, et al. Automated Four-Point probe Measurement of Nanowires Inside a Scanning Electron Microscope[J]. IEEE Transactions on Nanotechnology, 2011, 10(4): 674-681.

    Google Scholar

    [5] Gong Z, Chen B K, Sun Y, et al. Fluorescence and SEM correlative microscopy for nanomanipulation of subcellular structures[J]. Light: Science and Application, 2014, 3(11): e224. doi: 10.1038/lsa.2014.105

    CrossRef Google Scholar

    [6] Cizmar P, Vladar A E, Postek M T. Real-Time Scanning Charged-Particle Microscope Image Composition with Correction of Drift[J]. Microscopy and Microanalysis, 2011, 17(2): 302-308. doi: 10.1017/S1431927610094250

    CrossRef Google Scholar

    [7] Marturi N, Demele S, Piat N. Fast Image Drift Compensation in Scanning Electron Microscope using Image Registration[C]// Proceedings of 2013 IEEE International Conference on Automation Science and Engineering, 2013: 807-812.

    Google Scholar

    [8] 肖春宝, 冯大政, 冯祥卫.重抽样优化的快速随机抽样一致性算法[J].计算机辅助设计与图形学学报, 2016, 28(4): 607-613.

    Google Scholar

    Xiao C B, Feng D Z, Feng X W. Fast RANSAC algorithm with resample optimization[J]. Journal of Computer-Aided Design and Computer Graphics, 2016, 28(4): 607-613.

    Google Scholar

    [9] Sutton M A, Li N, Li X D. Metrology in a scanning electron microscope: theoretical developments and experimental validation[J]. Measurement Science and Technology, 2006, 17(10): 2613-2622. doi: 10.1088/0957-0233/17/10/012

    CrossRef Google Scholar

    [10] Malti A C, Dembele S, Piat N, et al. Magnification-continuous static calibration model of a scanning- electron microscope[J]. Journal of Electronic Imaging, 2012, 21(3): 033020.

    Google Scholar

    [11] Marturi N. Vision and visual servoing for nanomanipulation and nanocharacterization in scanning electron microscope[D]: France: Univerite De France-comte, 2013.

    Google Scholar

    [12] Liu W, Zhao W J, Li C, et al. Detecting small moving target based on the improved ORB feature matching[J]. Opto-Electronic Engineering, 2015, 42(10): 13-20.

    Google Scholar

  • Overview: With the development of nanoscience, new nanomaterials and their excellent properties have been continuously discovered and recognized, showing a broad application prospect. Compared with the traditional optical microscope, the scanning electron microscope (SEM) has been widely used in the characterization of nanomaterials, micro deformation measurement with the advantages of its simple preparation, wide range of adjustable magnification, high resolution and great depth of field. In addition, the use of SEM images as visual sensors has merit in the development of automated nanomanipulations, such as automatic detection of IC chips, automatic pick-up of nanowires, and automatic measurement of nanowire impedance characteristics by four-point probes, making humans liberated from tedious nano-manipulations, greatly improving work efficiency. However, the drift of images caused by electron beam drift and electromagnetic interference at high magnification will affect the size measurement and characterization of nanomaterials in SEM.

    At present, domestic and foreign scholars have actively studied the correction method of SEM image drift, but there are still many obvious shortcomings. Cizmar et al. aligned the images correctly to form a single image by using larges of image frames, but which can't meet the requirements of the real-time in SEM. In order to solve the problem of real time, Naresh took the ORB combing the RANSAC to correct the drift image, but it does not eliminate the external points. The increase of the external points lead to the rapid increase of the sampling times of RANSAC, and then affects the efficiency of the algorithm and the accuracy of the measurement. Although AFM compensation algorithm has been able to solve the problem of image drift, it is not suitable for SEM because of the different principles. An image shift correction algorithm based on ORB (oriented FAST and rotated BRIEF) combing the PROSAC (progressive sample consensus) is proposed in this paper. Firstly, the ORB algorithm is used to detect the feature between the reference image and real-time image. Then the initial matching of the feature is implemented by using the Hamming distance and cross-matching. Moreover, the RANSAN (random sample consensus) optimization algorithm PROSAC is used to calculate the homography matrix between frames and the final exact homography matrix is re-iterated after eliminating exterior point. Finally, the SEM image drift is corrected in real time using the perspective transformation of the homography matrix. The experiments show that the proposed algorithm is high precision and satisfies the requirement of SEM real-time processing. This method can meet the requirements of the drift correction in SEM under different magnifications, and provide help for SEM measurement, characterization and other applications.

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(7)

Tables(8)

Article Metrics

Article views(11243) PDF downloads(3232) Cited by(0)

Access History

Other Articles By Authors

Article Contents

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint