Abstract
Fast pixelated detectors incorporating direct electron detection (DED) technology are increasingly being regarded as universal detectors for scanning transmission electron microscopy (STEM), capable of imaging under multiple modes of operation. However, several issues remain around the post-acquisition processing and visualization of the often very large multidimensional STEM datasets produced by them. We discuss these issues and present open source software libraries to enable efficient processing and visualization of such datasets. Throughout, we provide examples of the analysis methodologies presented, utilizing data from a 256 × 256 pixel Medipix3 hybrid DED detector, with a particular focus on the STEM characterization of the structural properties of materials. These include the techniques of virtual detector imaging; higher-order Laue zone analysis; nanobeam electron diffraction; and scanning precession electron diffraction. In the latter, we demonstrate a nanoscale lattice parameter mapping with a fractional precision ≤6 × 10−4 (0.06%).
Highlights
One of the greatest revolutions in scanning transmission electron microscopy (STEM) in recent years is the development and use of fast pixelated detectors incorporating direct electron detection (DED) technology, and these are rapidly becoming a key component of the imaging system for a modern STEM
Using test data obtained in the scanning precession electron diffraction (SPED) mode of acquisition (Vincent & Midgley, 1994) of a custom system (MacLaren et al, 2020), we demonstrate that a lattice parameter fractional precision of 6 ×10−4 is possible using a probe with a spatial resolution of 1.1 nm
We provided examples of their use across a number of applications in the area of structural characterization, including the techniques of virtual detector imaging for BF and DF imaging, HOLZ analysis for extraction of structural information along the path of the beam, and nanobeam and scanning precession electron diffraction for lattice parameter determination and strain analysis
Summary
One of the greatest revolutions in scanning transmission electron microscopy (STEM) in recent years is the development and use of fast pixelated detectors incorporating direct electron detection (DED) technology, and these are rapidly becoming a key component of the imaging system for a modern STEM. Ophus (2019) has provided an excellent review of the area, and Tate et al (2016), Yang et al (2015), and Krajnak et al (2016) have described some suitable detectors for different applications in pixelated STEM imaging. The best fractional strain precision reported is with SPED at 2 × 10−4 (Rouvière et al, 2013); very recent work with patterned probes (Guzzinati et al, 2019; Zeltmann et al, 2020) have achieved precisions approaching the same value Analysis of such 2D lattice images can be performed with a range of packages, including Atomap, optimized for atomic resolution STEM imaging (Nord et al, 2017); CrysTBox for HRTEM, SAED, and convergent beam electron diffraction (CBED) imaging (Klinger, 2017); library-based approaches to crystal phase and orientation identification (Rauch et al, 2010); and ones recently developed for 4D-STEM (Savitzky et al, 2019; Zeltmann et al, 2020). Compared to applying ML approaches to images directly, applying them to the extracted data would vastly reduce the size of the data to be processed (by a factor of several thousands), and automatically accounts for any de-scan in the measurement as the calculated basis vectors are insensitive to the pattern center position
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