Experimental Mechanics

, Volume 51, Issue 1, pp 97–109 | Cite as

Quantitative Stereovision in a Scanning Electron Microscope

  • T. Zhu
  • M. A. SuttonEmail author
  • N. Li
  • J.-J. Orteu
  • N. Cornille
  • X. Li
  • A. P. Reynolds


Accurate, 3D full-field measurements at the micron-level are of interest in a wide range of applications, including both facilitation of mechanical experiments at reduced length scales and accurate profiling of specimen surfaces. Scanning electron microscope systems (SEMs) are a natural platform for acquiring high magnification images for stereo-reconstruction. In this work, an integrated methodology for accurate three-dimensional metric reconstruction and deformation measurements using single column SEM imaging systems is described. In these studies, the specimen stage is rotated in order to obtain stereo views of the specimen as it undergoes mechanical or thermal loading. Simulations and preliminary experimental studies at 300× demonstrate that (a) spatially-varying image distortions can be removed from images using a non-parametric distortion model, (b) the system can be reliably calibrated using distortion-corrected images of a planar object and grid at various orientations and (c) specimen rotation variability during the measurement phase can be controlled so that baseline strain errors are within the range of ±150 µε. Benchmark rigid body motion experiments using calibrated SEM views demonstrate that all components of strain in the reconstructed object have a mean value around O(10−4) and a random spatial distribution with standard deviation ≈ 300 micro-strain.


Scanning electron microscope (SEM) Stereo-vision Digital image correlation (DIC) Strain measurement Accurate 3D topography and 3D displacement measurements 



The technical support of Dr. Hubert Schreier and Correlated Solutions Incorporated is deeply appreciated. The financial support provided by (a) Dr. Stephen Smith through NASA NNX07AB46A, (b) Sandia National Laboratory and Dr. Timothy Miller and Dr. Phillip Reu through Sandia Contract PO#551836 and (c) Dr. Bruce Lamattina through ARO# W911NF-06-1-0216 are gratefully acknowledged. In addition, the research support provided by the Department of Mechanical Engineering at the University of South Carolina is also gratefully acknowledged.


  1. 1.
    Chasiotis I (2004) Mechanics of thin films and microdevices. IEEE Trans on Device and Mater Reliab 4(2):176–188CrossRefGoogle Scholar
  2. 2.
    Hemker KJ, Sharpe WN (2007) Microscale characterization of mechanical properties. Annu Rev Mater Res 37:93CrossRefGoogle Scholar
  3. 3.
    Sharpe WN (2003) Murray lecture tensile testing at the micrometer scale: Opportunities in Exp Mech. Exp Mech 43(3):228–237MathSciNetGoogle Scholar
  4. 4.
    Sutton MA, Li N, Garcia D et al (2006) Metrology in a scanning electron microscope: theoretical developments and experimental validation. Meas Sci & Tech 17(10):2613–2622CrossRefGoogle Scholar
  5. 5.
    Kang J, Ososkov J, Embury J et al (2007) Digital image correlation studies for microscopic strain distribution and damage in dual phase steels. Scr Mater 56(11):999–1002CrossRefGoogle Scholar
  6. 6.
    Sabate N, Vogel D, Gollhardt A et al (2006) Digital image correlation of nanoscale deformation fields for local stress measurement in thin films. Nanotechnol 17(20):5264CrossRefGoogle Scholar
  7. 7.
    Bao G, Suresh S (2003) Cell and molecular mechanics of biological materials. Nat Mater 2(11):715–725CrossRefGoogle Scholar
  8. 8.
    Chen CS, Mrksich M, Huang S et al (1997) Geometric control of cell life and death. Sci 276(5317):1425CrossRefGoogle Scholar
  9. 9.
    Dikovsky D, Bianco-Peled H, Seliktar D (2008) Defining the role of matrix compliance and proteolysis in three-dimensional cell spreading and remodeling. Biophys J 94(7):2914CrossRefGoogle Scholar
  10. 10.
    Pedersen JA, Swartz MA (2005) Mechanobiology in the third dimension. Ann of Biomed Eng 33(11):1469–1490CrossRefGoogle Scholar
  11. 11.
    Yeung T, Georges PC, Flanagan LA et al (2005) Effects of substrate stiffness on cell morphology, cytoskeletal structure, and adhesion. Cell Motil Cytoskelet 60(1):24–34CrossRefGoogle Scholar
  12. 12.
    Marinello F, Bariani P, Savio E et al (2008) Critical factors in SEM 3D stereo microscopy. Meas Sci Tech 19(6):65705CrossRefGoogle Scholar
  13. 13.
    Raspanti M, Binachi E, Gallo I et al (2005) A vision-based, 3D reconstruction technique for scanning electron microscopy: direct comparison with atomic force microscopy. Microsc Res Tech 67(1):1CrossRefGoogle Scholar
  14. 14.
    Ponz E, Ladaga JL, Bonetto RD (2005) Measuring surface topography with scanning electron microscopy. I. EZEImage: a program to obtain 3D surface data. Microsc and Microanal 12(02):170–177CrossRefGoogle Scholar
  15. 15.
    Villarrubia JS, Vladar AE, Postek MT (2005) Scanning electron microscope dimensional metrology using a model-based library. Surf Interface Anal 37(11):951–958CrossRefGoogle Scholar
  16. 16.
    Bariani P, De Chiffre L, Hansen HN et al (2005) Investigation on the traceability of three dimensional scanning electron microscope measurements based on the stereo-pair technique. Precis Eng 29(2):219–228CrossRefGoogle Scholar
  17. 17.
    Sinram O, Ritter M, Kleindiek S et al (2002) Calibration of an SEM, using a nano positioning tilting table and a microscopic calibration pyramid. Int Arch Photogramm Remote Sens Spat Info Sci 34(5):210–215Google Scholar
  18. 18.
    Scherer S (2002) 3D surface analysis in scanning electron microscopy. GIT Imaging Microsc 3:45–46Google Scholar
  19. 19.
    Scherer S, Werth P, Pinz A et al (1999) Automatic surface reconstruction using SEM images based on a new computer vision approach. Inst of Phys Pub IncGoogle Scholar
  20. 20.
    Password F (1999) Three-dimensional morphometry in scanning electron microscopy: a technique for accurate dimensional and angular measurements of microstructures using stereopaired digitized images and digital image analysis. J Microsc 195(1):23–33CrossRefGoogle Scholar
  21. 21.
    Kayaalp AE, Rao AR, Jain R (1990) Scanning electron microscope-based stereo analysis. Mach Vis App 3(4):231–246CrossRefGoogle Scholar
  22. 22.
    Kolednik O (1981) A contribution to stereophotogrammetry with the scanning electron microscope. Prakt Metallogr 18(12):562–573Google Scholar
  23. 23.
    Boyde A, Ross HF (1975) Photogrammetry and the scanning electron microscope. Photogramm Rec 8(46):408–408CrossRefGoogle Scholar
  24. 24.
    Piazzesi G (1973) Photogrammetry with the scanning electron microscope. J Phys E Sci Instrum 6:392–396CrossRefGoogle Scholar
  25. 25.
    Maune DF (1973) Photogrammetric self-calibration of a scanning electron microscope. Univ Microfilm IntGoogle Scholar
  26. 26.
    MeX software; Alicona Imaging;
  27. 27.
    Lockwood WD, Reynolds AP (1999) Use and verification of digital image correlation for automated 3-d surface characterization in the scanning electron microscope. Mater Charact 42(2–3):123–134CrossRefGoogle Scholar
  28. 28.
    Faugeras O, Luong QT, Papadopoulo T (2001) The geometry of multiple images. MIT, CambridgezbMATHGoogle Scholar
  29. 29.
    Sutton MA, Li N, Joy DC et al (2007) Scanning electron microscopy for quantitative small and large deformation measurements part I: SEM imaging at magnifications from 200 to 10, 000. Exp Mech 47(6):775–787CrossRefGoogle Scholar
  30. 30.
    Sutton MA, Li N, Garcia D et al (2007) Scanning electron microscopy for quantitative small and large deformation measurements Part II: experimental validation for magnifications from 200 to 10, 000. Exp Mech 47(6):789–804CrossRefGoogle Scholar
  31. 31.
    Sutton MA, Orteu JJ, Schreier HW (2009) Image correlation for shape, motion and deformation measurements: basic concepts, theory and practical applications. Springer, New York. ISBN 978-0-387-78747-3Google Scholar
  32. 32.
    Sutton MA, Correlation DI, Sharpe WN Jr (eds) (2008) Springer handbook of experimental solid mechanics. Springer, Berlin. ISBN 978-0-387-26883-5Google Scholar
  33. 33.
    Faugeras O (1993) Three-dimensional computer vision: a geometric viewpoint. MIT, CambridgeGoogle Scholar
  34. 34.
    Helm JD, McNeill SR, Sutton MA (1996) Improved three-dimensional image correlation for surface displacement measurement. Opt Eng 35:1911CrossRefGoogle Scholar
  35. 35.
    Schreier HW, Garcia D, Sutton MA (2004) Advances in light microscope stereovision. Exp Mech 44(3):278–288CrossRefGoogle Scholar
  36. 36.
    Triggs B, McLauchlan P, Hartley R et al (1999) Bundle Adjustment-A modern synthesis. Lecture notes in computer science. p. 298–372Google Scholar
  37. 37.
    Schreier HW, Sutton MA (2002) Systematic errors in digital image correlation due to undermatched subset shape functions. Exp Mech 42(3):303–310CrossRefGoogle Scholar
  38. 38.
    Schreier HW, Braasch JR, Sutton MA (2000) Systematic errors in digital image correlation caused by intensity interpolation. Opt Eng 39:2915CrossRefGoogle Scholar
  39. 39.
    Wang YQ, Sutton MA, Schreier HW (2009) Quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements quantitative error assessment in pattern matching: effects of intensity pattern noise, interpolation, strain and image contrast on motion measurements. J Strain 45:160–178CrossRefGoogle Scholar
  40. 40.
    VIC-2D and VIC-3D, Correlated Solutions Inc., West Columbia, SC,
  41. 41.
    Applied Image Inc., Rochester, NYGoogle Scholar

Copyright information

© Society for Experimental Mechanics 2010

Authors and Affiliations

  • T. Zhu
    • 1
  • M. A. Sutton
    • 1
    Email author
  • N. Li
    • 1
  • J.-J. Orteu
    • 2
  • N. Cornille
    • 3
  • X. Li
    • 1
  • A. P. Reynolds
    • 1
  1. 1.Department of Mechanical EngineeringUniversity of South CarolinaColumbiaUSA
  2. 2.Université de Toulouse, INSA, UPS, Mines Albi, ISAE, ICA (Institut Clément Ader)AlbiFrance
  3. 3.G2MétricLaunaguetFrance

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