Low magnification differential phase contrast imaging of electric fields in crystals with fine electron probes

Taplin, Daniel J.; Shibata, Naoya; Weyland, Matthew; Findlay, Scott

doi:10.1016/j.ultramic.2016.07.010

“…The technique is also capable of measuring long‐range fields, however. In this case, sample and experimental parameters must be carefully taken into account, especially if interfaces are present, since scattering information due to crystal structure can be convolved with that originating from fields . Nevertheless, measurement of long‐range electric fields has been demonstrated, for example as shown in Figure .…”

Section: Emerging Stem and Eels Techniquesmentioning

confidence: 99%

Emerging Electron Microscopy Techniques for Probing Functional Interfaces in Energy Materials

Zachman

¹

,

Hachtel

²

,

Idrobo

³

et al. 2019

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Interfaces play a fundamental role in many areas of chemistry. However, their localized nature requires characterization techniques with high spatial resolution in order to fully understand their structure and properties. State‐of‐the‐art atomic resolution or in situ scanning transmission electron microscopy and electron energy‐loss spectroscopy are indispensable tools for characterizing the local structure and chemistry of materials with single‐atom resolution, but they are not able to measure many properties that dictate function, such as vibrational modes or charge transfer, and are limited to room‐temperature samples containing no liquids. Here, we outline emerging electron microscopy techniques that are allowing these limitations to be overcome and highlight several recent studies that were enabled by these techniques. We then provide a vision for how these techniques can be paired with each other and with in situ methods to deliver new insights into the static and dynamic behavior of functional interfaces.

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“…PC imaging of electric fields at atomic resolution is fairly straightforward with at hin sample.T he technique is also capable of measuring long-range fields,however.Inthis case, sample and experimental parameters must be carefully taken into account, especially if interfaces are present, since scattering information due to crystal structure can be convolved with that originating from fields. [52,83,84] Nevertheless,m easurement of long-range electric fields has been demonstrated, [85,86] for example as shown in Figure 3. Figure 3c.Aclear feature is observed in the horizontal, but not vertical, direction at the junction, due to the built-in horizontal electric field from the difference in dopants across the interface (fainter vertical features can also be seen near the junction from variations in doping concentration).…”

Section: Angewandte Chemiementioning

confidence: 99%

Emerging Electron Microscopy Techniques for Probing Functional Interfaces in Energy Materials

Zachman

¹

,

Hachtel

²

,

Idrobo

³

et al. 2019

View full text Add to dashboard Cite

Interfaces play a fundamental role in many areas of chemistry. However, their localized nature requires characterization techniques with high spatial resolution in order to fully understand their structure and properties. State‐of‐the‐art atomic resolution or in situ scanning transmission electron microscopy and electron energy‐loss spectroscopy are indispensable tools for characterizing the local structure and chemistry of materials with single‐atom resolution, but they are not able to measure many properties that dictate function, such as vibrational modes or charge transfer, and are limited to room‐temperature samples containing no liquids. Here, we outline emerging electron microscopy techniques that are allowing these limitations to be overcome and highlight several recent studies that were enabled by these techniques. We then provide a vision for how these techniques can be paired with each other and with in situ methods to deliver new insights into the static and dynamic behavior of functional interfaces.

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“…This is particularly useful for PACBED analysis where LSF may be essential and/or is the path of least resistance. For example, this includes polarity determination [6], electrostatic field quantification [7], or quantifying octahedral distortion [3-5] Figure 10: Hybrid CNN+LSF architecture for PACBED measurement.…”

Section: Hybrid Cnn+lsf Analysismentioning

confidence: 99%

“…These patterns depend sensitively upon the position of the probe within the unit cell, but by averaging these patterns together, a position averaged CBED (PACBED) pattern is created [1]. The patterns then depend strongly on sample thickness and tilt, and also reveal crystal polarity, changes in composition, octahedral distortions, and strain [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns

Xu

¹

,

LeBeau

²

2018

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We establish a series of deep convolutional neural networks to automatically analyze position averaged convergent beam electron diffraction patterns. The networks first calibrate the zero-order disk size, center position, and rotation without the need for pretreating the data. With the aligned data, additional networks then measure the sample thickness and tilt. The performance of the network is explored as a function of a variety of variables including thickness, tilt, and dose. A methodology to explore the response of the neural network to various pattern features is also presented. Processing patterns at a rate of  ∼ 0.1 s/pattern, the network is shown to be orders of magnitude faster than a brute force method while maintaining accuracy. The approach is thus suitable for automatically processing big, 4D STEM data. We also discuss the generality of the method to other materials/orientations as well as a hybrid approach that combines the features of the neural network with least squares fitting for even more robust analysis. The source code is available at https://github.com/subangstrom/DeepDiffraction.

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Low magnification differential phase contrast imaging of electric fields in crystals with fine electron probes

Cited by 11 publications

References 38 publications

Emerging Electron Microscopy Techniques for Probing Functional Interfaces in Energy Materials

Emerging Electron Microscopy Techniques for Probing Functional Interfaces in Energy Materials

Emerging Electron Microscopy Techniques for Probing Functional Interfaces in Energy Materials

A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns

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