Improved EBSD indexation accuracy by considering energy distribution of diffraction patterns

Qiwei Shi (1, 2) , Liyao Jiao (1) , Dominique Loisnard (3) , Chengyi Dan (1) , Zhe Chen (1) , Haowei Wang (1) , Stéphane Roux (4)

1 SJTU - School of Materials Science and Engineering, Shanghai Jiao Tong University
2 SJTU - SJTU - ParisTech Elite Institute of Technology
3 EDF R&D, Site des Renardières
4 LMPS - Laboratoire de Mécanique Paris-Saclay

Registering experimental and simulated electron diffraction patterns is increasingly used for advanced electron backscatter diffraction indexation (EBSD) analysis, yet the accuracy of registration is limited by several effects not accounted for in pattern simulation, such as the Kikuchi band asymmetry, gray level reversal, optical distortion and non-uniform electron energy. Though some of these phenomena can be simulated by Monte Carlo method and dynamical simulation, the computation is highly demanding and their effects on EBSD calibration are seldom analyzed. Here a simple weighting of energy for simulated diffraction pattern is proposed based on several master patterns calculated beforehand , to effectively account for the electron energy distribution. Integrated digital image correlation is alternatively applied to quantify the electron energy field and calibrate geometry parameters. A metric of the accuracy of indexed crystal orientation is proposed based on a large-area high-definition experimental EBSD acquisition on a (100)-face single crystal Si wafer. The consideration of inhomogeneous energy distribution reduces the crystal orientation discrepancy by 0.128°for the Si dataset.

Mots clés: EBSD calibration; High-angular-resolution EBSD; Integrated digital image correlation; electron energy distribution; error analysis

Domaines: Science des matériaux [cond-mat.mtrl-sci]; Autre [cond-mat.other]

Qiwei Shi, Liyao Jiao, Dominique Loisnard, Chengyi Dan, Zhe Chen, et al.. Improved EBSD indexation accuracy by considering energy distribution of diffraction patterns. Materials Characterization, 2022, 188, ⟨10.1016/j.matchar.2022.111909⟩. ⟨hal-03649313⟩