Elevated temperature transmission Kikuchi diffraction in the SEM

Electron Backscatter Diffraction (EBSD) is a technique to obtain microcrystallographic information from materials by collecting large-angle Kikuchi patterns in the scanning electron microscope (SEM). An important fundamental question concerns the scattering-angle dependent electron energy distribution which is relevant for the formation of the Kikuchi diffraction patterns. Here we review the existing experimental data and explore the effective energy spectrum that is operative in the generation of backscatter Kikuchi patterns from silicon. We use a full pattern comparison of experimental data with dynamical electron diffraction simulations. Our energy-dependent cross-correlation based pattern matching approach establishes improved constraints on the effective Kikuchi pattern energy spectrum which is relevant for high-resolution EBSD pattern simulations and their applications. * a.winkelmann@lzh.de †

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“…• The quality of Kikuchi pattern features is influenced by temperature effects [46] such as increased thermal vibrations.…”

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confidence: 99%

Constraints on the effective electron energy spectrum in backscatter Kikuchi diffraction

2019

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“…Recent investigations on the depth resolution of on-axis TKD [11] and on the range of diffraction contrast depending on sample and microscope parameters for on-axis TKD have been presented [12]. Furthermore, due to enhanced signal and speed, it was also shown that this configuration might be more beneficial for dynamic TKD investigation [13] and can also be advantageous for non-conductive samples and weakly diffracting materials.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Finally, the requirement of the on-axis TKD detector movement between the two positions makes it impossible to acquire an orientation map and high-quality BF/DF images simultaneously. As previously pointed out [13], this limitation can be very critical in dynamic investigations, first because STEM images can be acquired much faster than TKD measurements, and second because in many situations the imaging-mapping combination is essential for a thorough understanding of the dynamic evolution of the microstructure.…”

Section: Accepted Manuscript Distances With and Without Immersion Mode (Sample Immersed In Magnetic Field) (Single Column)mentioning

confidence: 99%

Improving the imaging capability of an on-axis transmission Kikuchi detector

et al. 2019

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Transmission Kikuchi Diffraction (TKD) in the scanning electron microscope has been developing at a fast pace since its introduction less than a decade ago. The recently presented on-axis detector configuration, with its optimized geometry, has significantly increased the signal yield and facilitated the acquisition of STEM images in bright field (BF) and dark field (DF) mode, in addition to the automated orientation mapping of nanocrystalline electron transparent samples. However, the physical position of the integrated imaging system, located outside the detector screen, requires its movement in order to combine high resolution STEM images with high resolution orientation measurements. The difference between the two positions makes it impossible to acquire optimal signals simultaneously, leading to challenges when investigating site-specific nanocrystalline microstructures. To eliminate this drawback, a new imaging capability was added at the centre of the on-axis TKD detector, thus enabling acquisition of optimal quality BF images and orientation maps without detector movement. The advantages brought about by this new configuration are presented and the associated limitations are discussed.

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“…Recently, TKD was optimized by changing the geometric configuration from a conventional off-axis to an on-axis detector geometry, which improves the lateral resolution, acquisition speed, and pattern quality (Fundenberger et al, 2015, 2016; Brodu et al, 2017; Yuan et al, 2017). However, as the source of TKD patterns is at the exit surface of the sample, the film surface has a strong influence on the quality of the patterns and thus the reliability of the maps (Keller & Geiss, 2012; Sneddon et al, 2016; Fanta et al, 2018; Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%