edfas.org 21 ELECTRONIC DEVICE FAILURE ANALYSIS | VOLUME 25 NO. 3 where symmetry breaking occurs. Meaning it can map regions where atomic displacements are present, which might originate from defects, octahedral rotations, or strain. It has even been suggested as a method to identify point defects.[53] Machine learning (ML) also has been tasked in identifying changes to symmetry in the diffraction patterns as a tool to identify defects. For example, Li et al. employed a manifold learning approach to 4D-STEM data collected from graphene in order to classify the patterns based on the local symmetry of features observed within the direct beam of the diffraction pattern.[54] By doing so, they were able to map interesting variations not obvious by simple visual inspection of the data including those due to point defects. One issue with this approach is that patterns that differ only by in-plane rotation will be separated into different classes even though they are identical otherwise. To address this redundancy, artificial neural networks such as rotationally invariant variational autoencoders have been explored. These were successful in identifying deviations in symmetry in graphene resulting from both vacancies and impurities.[55] Because 4D-STEM datasets are large, typically many GBs of data, this presents challenges to detect minority features, like defects. ML is one potential solution to this challenge and presents both the opportunity to accelerate as well as automate aspects of data reduction and categorization. SUMMARY 4D-STEM provides researchers with an immense wealth of information that can be analyzed in a multitude of ways to characterize a sample’s structure. Herein were examples of imaging, strain measurement, and defect analysis. Hardware and technique development are ongoing, and the fidelity, sensitivity, and speed at which these measurements can be made is expected to improve. In many of these cited examples the publishing authors have made available open-source versions of the software used to analyze the data, making these approaches readily accessible for one’s own research. The scope of part one did not capture the full application space of 4D-STEM as it can also measure other phenomena such as internal electromagnetic fields, which will be discussed in an upcoming second part of this review. REFERENCES 1. J.M. Cowley, M.A. Osman, and P. Humble: “Nanodiffraction from Platelet Defects in Diamond,” Ultramicroscopy, 1984, 15(4), p. 311-318. 2. J. Konnert, et al.: “Determination of Atomic Positions using Electron Nanodiffraction Patterns from Overlapping Regions: Si[110],” Ultramicroscopy, 1989, 30(3), p. 371-384. 3. J. Konnert and P. D’Antonio: “Image Reconstruction using Elec- tron Microdiffraction Patterns from Overlapping Regions,” Ultramicroscopy, 1986, 19(3), p. 267-277. 4. J.M. Rodenburg, B.C. McCallum, and P.D. Nellist: “Experimental Tests on Double-resolution Coherent Imaging via STEM,” Ultramicroscopy, 1993, 48(3), p. 304-314. 5. P.D. Nellist, B.C. McCallum, and J.M. Rodenburg: “Resolution Beyond the ‘Information Limit’ in Transmission Electron Microscopy,” Nature, 1995, 374(6523), p. 630-632. 6. N.J. Zaluzec: “Quantitative Measurements of Magnetic Vortices using Position Resolved Diffraction in Lorentz Stem,” Microscopy and Microanalysis, 2002, 8(S02), p. 376-377. 7. W. Chen, et al.: “High Resistivity Silicon Active Pixel Sensors for Recording Data from STEM,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2003, 512(1), p. 368-377. 8. W. Chen, et al.: “Development of X-ray Active Matrix Pixel Sensors for Detection of Electrons in Scanning Transmission Electron Microscope,” in IEEE Nuclear Science Symposium Conference Record, 2005, 23-29 Oct. 2005, 3, p. 1431-1435, doi: 10.1109/NSSMIC.2005.1596589. 9. P. Rehak, J.S. Wall, and Y. Zhu: “Direct Detectors of Electrons for STEM and TEM,” Microscopy and Microanalysis, 2005, 11(S02), p. 470-471. 10. T.A. Caswell, et al.: “A High-speed Area Detector for Novel Imaging Techniques in a Scanning Transmission Electron Microscope,” Ultramicroscopy, 2009, 109(4), p. 304-311. 11. A.M. Raighne, et al.: “Medipix2 as a Highly Flexible Scanning/ Imaging Detector for Transmission Electron Microscopy,” Journal of Instrumentation, 2011, 6(01), p. C01047. 12. K. Müller-Caspary, A. Oelsner, and P. Potapov: “Two-dimensional Strain Mapping in Semiconductors by Nano-beam Electron Diffraction Employing a Delay-line Detector,” Applied Physics Letters, 2015, 107(7), p. 072110. 13. I. MacLaren, et al.: “Detectors—The Ongoing Revolution in Scanning Transmission Electron Microscopy and Why this Important to Material Characterization,” APL Materials, 2020, 8(11), p. 110901. 14. C. Gammer, et al.: “Diffraction Contrast Imaging using Virtual Apertures,” Ultramicroscopy, 2015, 155, p. 1-10. 15. H. Yang, et al.: “4D STEM: High Efficiency Phase Contrast Imaging using a Fast Pixelated Detector,” Journal of Physics: Conference Series, 2015, 644(1), p. 012032. 16. K. Kimoto and K. Ishizuka: “Spatially Resolved Diffractometry with Atomic-column Resolution,” Ultramicroscopy, 2011, 111(8), p. 1111-1116. 17. Z. Chen, et al.: “Practical Aspects of Diffractive Imaging using an Atomic-scale Coherent Electron Probe,” Ultramicroscopy, 2016, 169, p. 107-121. 18. J.M. LeBeau, et al.: “Quantitative Comparisons of Contrast in Experimental and Simulated Bright-field Scanning Transmission Electron Microscopy Images,” Physical Review B, 2009, 80(17), p. 174106. 19. J. Tao, et al.: “Direct Imaging of Nanoscale Phase Separation in La0.55Ca0.45MnO3: Relationship to Colossal Magnetoresistance,” Physical Review Letters, 2009, 103(9), p. 097202. 20. J.M. Cowley: Diffraction Physics (Third Edition). Amsterdam: NorthHolland, 1995. 21. F.F. Krause and A. Rosenauer: “Reciprocity Relations in Transmission Electron Microscopy: A Rigorous Derivation,” Micron, 2017, 92, p. 1-5. 22. C. Ophus, et al.: “Efficient Linear Phase Contrast in Scanning Transmission Electron Microscopy with Matched Illumination and Detector Interferometry,” Nature Communications, 2016, 7(1), p. 10719. 23. M. Tomita, et al.: “Enhancement of Low-spatial-frequency Components by a New Phase-contrast STEM using a Probe Formed with an Amplitude Fresnel Zone Plate,” Ultramicroscopy, 2020, 218, p. 113089.
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