OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 9, Iss. 4 — Apr. 1, 2014

Fast and accurate metrology of multi-layered ceramic materials by an automated boundary detection algorithm developed for optical coherence tomography data

Peter Ekberg, Rong Su, Ernest W. Chang, Seok Hyun Yun, and Lars Mattsson  »View Author Affiliations


JOSA A, Vol. 31, Issue 2, pp. 217-226 (2014)
http://dx.doi.org/10.1364/JOSAA.31.000217


View Full Text Article

Enhanced HTML    Acrobat PDF (1643 KB) Open Access





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Optical coherence tomography (OCT) is useful for materials defect analysis and inspection with the additional possibility of quantitative dimensional metrology. Here, we present an automated image-processing algorithm for OCT analysis of roll-to-roll multilayers in 3D manufacturing of advanced ceramics. It has the advantage of avoiding filtering and preset modeling, and will, thus, introduce a simplification. The algorithm is validated for its capability of measuring the thickness of ceramic layers, extracting the boundaries of embedded features with irregular shapes, and detecting the geometric deformations. The accuracy of the algorithm is very high, and the reliability is better than 1 μm when evaluating with the OCT images using the same gauge block step height reference. The method may be suitable for industrial applications to the rapid inspection of manufactured samples with high accuracy and robustness.

© 2014 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(110.4500) Imaging systems : Optical coherence tomography
(120.3940) Instrumentation, measurement, and metrology : Metrology
(230.4170) Optical devices : Multilayers
(100.3008) Image processing : Image recognition, algorithms and filters

ToC Category:
Imaging Systems

History
Original Manuscript: August 22, 2013
Revised Manuscript: October 22, 2013
Manuscript Accepted: December 2, 2013
Published: January 6, 2014

Virtual Issues
Vol. 9, Iss. 4 Virtual Journal for Biomedical Optics

Citation
Peter Ekberg, Rong Su, Ernest W. Chang, Seok Hyun Yun, and Lars Mattsson, "Fast and accurate metrology of multi-layered ceramic materials by an automated boundary detection algorithm developed for optical coherence tomography data," J. Opt. Soc. Am. A 31, 217-226 (2014)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=josaa-31-2-217


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. S. Bredeau and L. Federzoni, “Multilayer: a large scale production of micro devices via new rolled multi material layered 3D shaping technology,” in Proceedings of the 4 M/ICOMM 2009 Conference, V. Saile, K. Ehmann, and S. Dimov, eds. (Karlsruhe, 2009), pp. 419–422.
  2. A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography: principles and applications,” Rep. Prog. Phys. 66, 239–303 (2003). [CrossRef]
  3. D. Stifter, “Beyond biomedicine: a review of alternative applications and developments for optical coherence tomography,” Appl. Phys. B 88, 337–357 (2007). [CrossRef]
  4. M. D. Duncan, M. Bashkansky, and J. Reintjes, “Subsurface defect detection in materials using optical coherence tomography,” Opt. Express 2, 540–545 (1998). [CrossRef]
  5. R. Su, M. Kirillin, P. Ekberg, A. Roos, E. Sergeeva, and L. Mattsson, “Optical coherence tomography for quality assessment of embedded microchannels in alumina ceramic,” Opt. Express 20, 4603–4618 (2012). [CrossRef]
  6. J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999). [CrossRef]
  7. J. A. Eichel, A. K. Mishra, D. A. Clausi, P. W. Fieguth, and K. K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in 2009 Canadian Conference on Computer and Robot Vision, F. Ferrie and M. Fiala, eds. (IEEE, 2009), pp. 313–320.
  8. M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009). [CrossRef]
  9. V. Kajić, B. Považay, B. Hermann, B. Hofer, D. Marshall, P. L. Rosin, and W. Drexler, “Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis,” Opt. Express 18, 14730–14744 (2010). [CrossRef]
  10. I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011). [CrossRef]
  11. A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17, 23719–23728 (2009). [CrossRef]
  12. M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1, 321–331 (1988). [CrossRef]
  13. A. Fard, C. Wang, O. Malik, G. Fu, A. Quach, K. Goda, and B. Jalali, “Near-100  MHz optical coherence tomography at 800  nm,” presented at the First International Symposium on Optical Coherence Tomography for Non-Destructive Testing, Linz, Austria, February13–14, 2013.
  14. Thorlabs, “Swept-source OCT systems,” Thorlabs Sweden AB, Mölndalsvägen 3, 400 20 Gothenburg, Sweden. http://www.thorlabs.de/newgrouppage9.cfm?objectgroup_id=2098 .
  15. U. Sharma, E. W. Chang, and S. H. Yun, “Long-wavelength optical coherence tomography at 1.7  μm for enhanced imaging depth,” Opt. Express 16, 19712–19723 (2008). [CrossRef]
  16. Swerea IVF, “Ceramic materials,” Swerea IVF, Mölndal (head office), P. O. Box 104, SE-431 22 Mölndal, Sweden. http://swerea.se/en/Start2/Working-Areas/Ceramics/ .
  17. R. Su, M. Kirillin, D. Jurków, K. Malecha, L. Golonka, and L. Mattsson, “Optical coherence tomography: a potential tool for roughness assessment of free and embedded surfaces of laser-machined alumina ceramic,” in Proceedings of the 8th International Conference on Multi-Material Micro Manufacture, H. Kück, H. Reinecke, and S. Dimov, eds. (Research, 2011), pp. 140–144.
  18. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008).
  19. P. Ekberg, “Ultra precision metrology—the key for mask lithography and manufacturing of high definition displays” (Licentiate Thesis, 2011), pp. 17–25.
  20. M. Wirth, M. Fraschini, M. Masek, and M. Bruynooghe, “Performance evaluation in image processing,” EURASIP J. Adv. Sig. Pr. 2006, 1–4 (2006). [CrossRef]
  21. L. Mattsson, V. Schulze, and J. Schneider, “Quality assurance and metrology,” in Ceramics Processing in Microtechnology (Whittles, 2009), Chap. 22, pp 305–325.
  22. T. Doiron and J. Beers, The Gauge Block Handbook (National Institute of Standards and Technology, 2009).
  23. Zygo NewView7300 3D optical surface profiler, http://www.zygo.com/?/met/profilers/newview7000/ .

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited