Automated Reconstruction of Spherical Kikuchi Maps

Abstract

An automated approach to reconstruct spherical Kikuchi maps from experimentally collected electron backscatter diffraction patterns and overlay each pattern onto its corresponding position on a simulated Kikuchi sphere is presented in this study. This work demonstrates the feasibility of warping any Kikuchi pattern onto its corresponding location of a simulated Kikuchi sphere and reconstructing a spherical Kikuchi map of a known phase based on any set of experimental patterns. This method consists of the following steps after pattern collection: 1) pattern selection based on multiple threshold values; 2) extraction of multiple scan parameters and phase information; 3) generation of a kinematically simulated Kikuchi sphere as the 'skeleton' of the spherical Kikuchi map; and 4) overlaying the inverse gnomonic projection of multiple selected patterns after appropriate pattern center calibration and refinement. In the case study of pure aluminum, up to 90% of the Kikuchi sphere could be reconstructed with just seven experimentally collected patterns. The proposed method is the first automated approach to reconstructing spherical Kikuchi maps from experimental Kikuchi patterns. It potentially enables 2 more accurate orientation calculation, new pattern center refinement methods, improved dictionary-based pattern matching, and phase identification in the future.

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

“…Increasing the number of frames averaged for each diffraction pattern increases the signal-to-noise ratio and results in better resolution of finer details in the diffraction patterns. Changing the detector tilt changes the relative position of the image with respect to the interaction volume of the sample, referred to as the pattern center in EBSD (Schwartz et al, 2009; Basinger et al, 2011; Zhu et al, 2019). For tilt angles that are far from the ideal conditions, the top or bottom edge of the EBSP may display blurring.…”

“…Hough-based indexing generally allows for phase differentiation of sufficiently distinct crystal structures (Britton et al, 2010;Foden et al, 2019b;Hielscher et al, 2019), but the process remains susceptible to structural misclassification (McLaren & Reddy, 2008;Chen & Thomson, 2010;Karthikeyan et al, 2013). 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.…”

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.…”

Indexation of electron diffraction patterns at grain boundaries

“…Experimental EBSPs can also be projected back to form master patterns [33,34]. Figure 2 shows the master pattern created by projecting back 50 experimental EBSPs, which are hand-picked to cover each grain of an EBSD acquisition using the same experimental setup reported in Refs.…”