Indexing Electron Backscatter Diffraction Patterns with a Refined Template Matching Approach

Abstract

Electron backscatter diffraction (EBSD) is a well-established characterisation technique used to gain information about the surface of crystalline materials. An electron beam is focussed on the surface of a sample, scattering with diffraction occurs in the near surface lattice generating electrons that exit the sample over a wide range of angles. An electron backscatter pattern (EBSP) can be collected on a screen and records the angular distribution of the emitted electrons. EBSPs correspond to direct projections of the lattice planes beyond the crystal; crystal orientation measurement and phase identification relies on the property that there are constant interplanar angles, which are invariant with respect to lattice rotation [1]. The indexing of EBSPs is usually done using an image processing technique called the Hough (a subset of the Radon) transform. The Hough transform converts lines seen in the pattern to points, which can be easily located by computationally methods. The interplanar angles are then found in "Hough-space" and compared to a look-up table to determine the phase and orientation of the lattice [2]. For most conventional uses, the Hough based indexing approach is sufficient, however, the Hough based indexing method often only considers the band centres, ignoring band width, intensity profile across bands and Secondary bands. This limits the accuracy and precision to how well the crystal orientation can be determined. It also limits accurate and precise measurements to be made on simple crystal structures where subtle features in the EBSP are not necessary for indexing [3].

Comparison of Orientation Mapping in SEM and TEM

“…Indexing “spot” patterns like those that are collected at 300 kV is more mathematically demanding. Typically, a “dictionary” approach is used, where a large number of simulated patterns of different orientations are generated and compared to the experimental patterns to find the best fit (Rauch & Dupuy, 2005; Chen et al, 2015; Marquardt et al, 2017; Ram et al, 2017; Friedrich et al, 2018; Jha et al, 2018; Ram & De Graef, 2018; Charpagne et al, 2019; Foden et al, 2019; Jackson et al, 2019; Zhu et al, 2019; Kaufmann et al, 2020). In Figure 7, an example is shown for two grains.…”

An Acquisition Parameter Study for Machine-Learning-Enabled Electron Backscatter Diffraction

“…However, it should be noted that it is only with careful design of a training set and rigorous validation that practitioners can be confident that the model has truly learned relevant information, is robust to new conditions, and has not found an unscientific approach to solving the problem [such as learning the presence of a ruler means a lesion is more likely cancerous (Esteva et al, 2017)] (Zech et al, 2018;Riley, 2019). The application of these tools to image-based tasks in materials science has proved to be useful for classification (Modarres et al, 2017;Ziletti et al, 2018;Foden et al, 2019a;Kaufmann et al, 2020a), segmentation (DeCost et al, 2019;Stan et al, 2020), and other objectives (Xie & Grossman, 2018;de Haan et al, 2019). Examples of techniques where interest in developing artificial intelligence agents for image-based tasks include optical microscopy (DeCost & Holm, 2015;DeCost et al, 2019), scanning transmission electron microscopy (STEM) (Laanait et al, 2019;Roberts et al, 2019), transmission electron microscopy (TEM) (Spurgeon et al, 2020), and electron backscatter diffraction (EBSD) (Shen et al, 2019;Ding et al, 2020;Kaufmann et al, 2020aKaufmann et al, , 2020bKaufmann et al, , 2020c.…”

“…Improvements to phase differentiation have been proposed and developed including dictionary indexing (Chen et al, 2015;Ram et al, 2017;Ram & De Graef, 2018;Singh et al, 2018) and spherical indexing (Day, 2008;Lenthe et al, 2019;Zhu et al, 2019), although each still requires a user to pre-select phases and further requires simulating the Kikuchi sphere for each selected phase. Recently, the EBSD community has begun to investigate the use of CNNs for indexing, phase differentiation, and determining components of the crystal structure (Foden et al, 2019a;Ding et al, 2020;Kaufmann et al, 2020aKaufmann et al, , 2020bKaufmann et al, , 2020c. It is a goal of several of these efforts that the onus of phase selection and/or structure determination can be at least partially lessened on the user (Kaufmann et al, 2020a(Kaufmann et al, , 2020b.…”

Kikuchi pattern simulations of backscattered and transmitted electrons

“…To improve the simulation‐based pattern matching approaches, it is necessary to understand which experimental effects are relevant to be included in the theoretical models used in Kikuchi diffraction pattern simulations. In this way, also the possible intrinsic accuracy and precision of simulations for pattern matching approaches can be better understood, including the development of new indexing approaches using synthetic test data 33–37 …”