Dr. Stuart Wright, Senior Scientist EBSD, EDAX

Recently I gave a webinar on dynamic pattern simulation. The use of a dynamic diffraction model [1, 2] allows EBSD patterns to be simulated quite well. One topic I introduced in that presentation was that of dictionary indexing [3]. You may have seen presentations on this indexing approach at some of the microscopy and/or materials science conferences. In this approach, patterns are simulated for a set of orientations covering all of orientation space. Then, an experimental pattern is tested against all of the simulated patterns to find the one that provides the best match with the experimental pattern. This approach does particularly well for noisy patterns.

I’ve been working on implementing some of these ideas into OIM Analysis™ to make dictionary indexing more streamlined for datasets collected using EDAX data collection software – i.e. OIM DC or TEAM™. It has been a learning experience and there is still more to learn.

As I dug into dictionary indexing, I recalled our first efforts to automate EBSD indexing. Our first attempt was a template matching approach [4]. The first step in this approach was to use a “Mexican Hat” filter. This was done to emphasize the zone axes in the patterns. This processed pattern was then compared against a dictionary of “simulated” patterns. The simulated patterns were simple – a white pixel (or set of pixels) for the major zone axes in the pattern and everything else was colored black. In this procedure the orientation sampling for the dictionary was done in Euler space.
It seemed natural to go this route at the time, because we were using David Dingley’s manual on-line indexing software which focused on the zone axes. In David’s software, an operator clicked on a zone axis and identified the <uvw> associated with the zone axis. Two zone axes needed to be identified and then the user had to choose between a set of possible solutions. (Note – it was a long time ago and I think I remember the process correctly. The EBSD system was installed on an SEM located in the botany department at BYU. Our time slot for using the instrument was between 2:00-4:00am so my memory is understandably fuzzy!)

One interesting thing of note in those early dictionary indexing experiments was that the maximum step size in the sampling grid of Euler space that would result in successful indexing was found to be 2.5°, quite similar to the maximum target misorientation for modern dictionary indexing. Of course, this crude sampling approach may have led to the lack of robustness in this early attempt at dictionary indexing. The paper proposed that the technique could be improved by weighting the zone axes by the sum of the structure factors of the bands intersecting at the zone axes.
However, we never followed up on this idea as we abandoned the template matching approach and moved to the Burn’s algorithm coupled with the triplet voting scheme [5] which produced more reliable results. Using this approach, we were able to get our first set of fully automated scans. We presented the results at an MS&T symposium (Microscale Texture of Materials Symposium, Cincinnati, Ohio, October 1991) where Niels Krieger-Lassen also presented his work on band detection using the Hough transform [6]. After the conference, we hurried back to the lab to try out Niels’ approach for the band detection part of the indexing process [7].
Modern dictionary indexing applies an adaptive histogram filter to the experimental patterns (at left in the figure below) and the dictionary patterns (at right) prior to performing the normalized inner dot-product used to compare patterns. The filtered patterns are nearly binary and seeing these triggered my memory of our early dictionary work as they reminded me of the nearly binary “Sombrero” filtered patterns– Olé!
We may not have come back full circle but progress clearly goes in steps and some bear an uncanny resemblance to previous ones. I doff my hat to the great work that has gone into the development of dynamic pattern simulation and its applications.

[1] A. Winkelmann, C. Trager-Cowan, F. Sweeney, A. P. Day, P. Parbrook (2007) “Many-Beam Dynamical Simulation of Electron Backscatter Diffraction Patterns” Ultramicroscopy 107: 414-421.
[2] P. G. Callahan, M. De Graef (2013) “Dynamical Electron Backscatter Diffraction Patterns. Part I: Pattern Simulations” Microscopy and Microanalysis 19: 1255-1265.
[3] S.I. Wright, B. L. Adams, J.-Z. Zhao (1991). “Automated determination of lattice orientation from electron backscattered Kikuchi diffraction patterns” Textures and Microstructures 13: 2-3.
[4] Y.H. Chen, S. U. Park, D. Wei, G. Newstadt, M.A. Jackson, J.P. Simmons, M. De Graef, A.O. Hero (2015) “A dictionary approach to electron backscatter diffraction indexing” Microscopy and Microanalysis 21: 739-752.
[5] S.I. Wright, B. L. Adams (1992) “Automatic-analysis of electron backscatter diffraction patterns” Metallurgical Transactions A 23: 759-767.
[6] N.C. Krieger Lassen, D. Juul Jensen, K. Conradsen (1992) “Image processing procedures for analysis of electron back scattering patterns” Scanning Microscopy 6: 115-121.
[7] K. Kunze, S. I. Wright, B. L. Adams, D. J. Dingley (1993) “Advances in Automatic EBSP Single Orientation Measurements.” Textures and Microstructures 20: 41-54.