Simulation of FIB-SEM Images for Segmentation of Porous Microstructures

  • Torben Prill
  • Katja Schladitz
  • Christian Wieser


FIB tomography yields high quality 3D images materials microstructures at the nanometer scale combining serial sectioning using a focused ion beam with scanning electron microscopy (SEM). However, SEM images represent the projection of a slice of unknown thickness. In FIB tomography of highly porous media this leads to shine-through-artifacts preventing automatic segmentation of the solid component. To overcome these difficulties, we simulate the SEM process. Monte-Carlo techniques yield accurate results, but are too slow for FIB-SEM requiring hundreds of SEM images for one dataset. Nevertheless, a quasi analytic description of the specimen and acceleration techniques cut down the computing time by orders of magnitude, allowing the simulation of FIB-SEM data. Based on simulated FIB-SEM image data, segmentation methods for the 3D microstructure of highly porous media from the FIB-SEM data can be developed and evaluated. Finally successful segementation enables quantitative analysis and numerical simulations of macroscopic properties.


scanning electron microscopy materials microstructures Boolean model FIB tomography quantitative analysis Monte-Carlo simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Ch. Wieser. Nanoporous and microporous materials in fuel cell applications. In New Congress Materials Science and Engineering, Nuremberg, September 4, 2008.Google Scholar
  2. [2]
    R. v. Helmolt U. Eberle. Fuel cell electric vehicles, battery electric vehicles, and their impact on energy storage technologies: An overview. Electric and Hybrid Vehicles, Chapter 9:227–245, 2010.CrossRefGoogle Scholar
  3. [3]
    J. Becker, Ch. Wieser, S. Fell, and K. Steiner. A multi-scale approach to material modeling of fuel cell diffusion media. International Journal of Heat and Mass Transfer, 54:1360–1368, 2011.CrossRefGoogle Scholar
  4. [4]
    G.B. Less, J.H. Seo, S. Han, A.M. Sastry, J. Zausch, A. Latz, S. Schmidt, Ch. Wieser, D. Kehrwald, and S. Fell. Micro-scale modeling of li-ion batteries: Parameterization and validation, publication in progress. Google Scholar
  5. [5]
    A. Latz J. Zausch J. Becker G.B. Less J.H. Seo S. Han A. M. Sastry S. Fell, K. Steiner. Porous materials of electrochemical cells in the cae design process. SIMVEC, 2010.Google Scholar
  6. [6]
    J. Becker, R. Flueckinger, M. Reum, F. Buechi, F. Marone, and M. Stampanoni. Determination of material properties of gas diffusion layers: Experiments and simulations using phase contrast tomographic microscopy. Journal of The Electrochemical Society, 156 (10):B1175–B1181, 2009.CrossRefGoogle Scholar
  7. [7]
    Couture A. R. Joly D. Tastet X. Aimez V. Drouin, D. and R. Gauvin. Casino v2.42a fast and easy-to-use modeling tool for scanning electron microscopy and microanalysis users. Scanning, 29:92–101, 2007.CrossRefGoogle Scholar
  8. [8]
    D. Gnieser, C. G. Frase, H. Bosse, and R. Tutsch. Mcsem- a modular monte carlo simulation program for various applications in sem metrology and sem photogrammetry. In Martina Luysberg, Karsten Tillmann, and Thomas Weirich, editors, EMC 2008 14th European Microscopy Congress 15 September 2008, Aachen, Germany, pages 549–550. Springer Berlin Heidelberg, 2008.CrossRefGoogle Scholar
  9. [9]
    Lowney J. Monsel-ii: Monte carlo simulation of sem signals for linewidth metrology. Microbeam Analysis, 4:131–136, 1995.Google Scholar
  10. [10]
    A. Karabekov, O. Zoran, Z. Rosenberg, and G. Eytan. Using monte carlo simulation for accurate critical dimension metrology of super small isolated poly-lines. Scanning, 25(6):291–296, 2003.CrossRefGoogle Scholar
  11. [11]
    D. C. Joy and S. Luo. An empirical stopping power relationship for low-energy electrons. Scanning, 11(4):176–180, 1989.CrossRefGoogle Scholar
  12. [12]
    D. Stoyan, W. S. Kendall, and J. Mecke. Stochastic Geometry and Its Applications. Wiley, Chichester, 2nd edition, 1995.Google Scholar
  13. [13]
    A. Seeger, C. Fretzagias, and R. Taylor. Software acceleration techniques for the simulation of scanning electron microscope images. Scanning, 25(5):264–273, 2003.CrossRefGoogle Scholar
  14. [14]
    A S Glassner. Space subdivision for fast ray tracing. IEEE Computer Graphics and Applications, 4:15–22, 1984.CrossRefGoogle Scholar
  15. [15]
    K. Iwata A. Fujimoto. Accelerated ray tracing. In Proc. CG Tokyo 85, 1985.Google Scholar
  16. [16]
    J. Amanatides and A. Woo. A fast voxel traversal algorithm for ray tracing. In In Eurographics87, pages 3–10, 1987.Google Scholar

Copyright information

© TMS (The Minerals, Metals & Materials Society) 2012

Authors and Affiliations

  • Torben Prill
    • 1
  • Katja Schladitz
    • 1
  • Christian Wieser
    • 2
  1. 1.Fraunhofer ITWM (Institute for Industrial Mathematics)KaiserslauternGermany
  2. 2.Adam Opel AG GM Alternative Propulsion Center EuropeRüsselsheimGermany

Personalised recommendations