Contrast transfer and noise considerations in focused-probe electron ptychography

O’Leary, Colum M.; Martinez, G.T.; Liberti, Emanuela; Humphry, M. J.; Kirkland, Angus I.; Nellist, Peter D.

doi:10.1016/j.ultramic.2020.113189

Cited by 46 publications

(29 citation statements)

References 51 publications

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“…For SSB reconstruction, it includes a process to integrate specific regions in the CBEDs according to their spatial frequency by performing Fourier transformation with respect to probe position. Certain spatial frequencies would gain from larger integration area, and thus creating a band-pass filtering e ect (Yang et al, 2015;O'Leary et al, 2021). The riCOM images of smaller kernel size (riCOM-21) are shown to be similar to the SSB results, also manifested by the similarity of their frequency spectra, as low frequency signal is suppressed.…”

Section: Comparison Of Reconstruction Methodsmentioning

confidence: 75%

Real Time Integration Centre of Mass (riCOM) Reconstruction for 4D-STEM

Yu¹,

Friedrich²,

Jannis³

et al. 2021

Preprint

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A real-time image reconstruction method for scanning transmission electron microscopy (STEM) is proposed. With an algorithm requiring only the center of mass (COM) of the di raction pa ern at one probe position at a time, it is able to update the resulting image each time a new probe position is visited without storing any intermediate di raction pa erns. The results show clear features at higher spatial frequency, such as atomic column positions. It is also demonstrated that some common post processing methods, such as band pass filtering, can be directly integrated in the real time processing flow. Compared with other reconstruction methods, the proposed method produces high quality reconstructions with good noise robustness at extremely low memory and computational requirements. An e icient, interactive open source implementation of the concept is further presented, which is compatible with framebased, as well as event-based camera/file types. This method provides the a ractive feature of immediate feedback that microscope operators have become used to, e.g. conventional high angle annular dark field STEM imaging, allowing for rapid decision making and fine tuning to obtain the best possible images for beam sensitive samples at the lowest possible dose.

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Section: Comparison Of Reconstruction Methodsmentioning

confidence: 75%

Real Time Integration Centre of Mass (riCOM) Reconstruction for 4D-STEM

Yu¹,

Friedrich²,

Jannis³

et al. 2021

Preprint

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show abstract

“…3(c,g) both take greater advantage of the 4D data and show much stronger signals. The difference in contrast of the SSB with respect to the iCOM signal arises from the different contrast transfer functions (CTF) of both signals [8,40] and the way in which the ptychographic method keeps track explicitly of where in probe reciprocal space each frequency is transferred [6,9]. The lower limit of the electron dose is calculated using the information from Fig.…”

Section: Resultsmentioning

confidence: 99%

Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwell time and faster scans for high precision and low dose applications

Jannis,

Hofer,

Gao

et al. 2021

Preprint

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Four dimensional scanning transmission electron microscopy (4D STEM) records the scattering of electrons in a material in great detail. The benefits offered by 4D STEM are substantial, with the wealth of data it provides facilitating for instance high precision, high electron dose efficiency phase imaging via center of mass or ptychography based analysis. However the requirement for a 2D image of the scattering to be recorded at each probe position has long placed a severe bottleneck on the speed at which 4D STEM can be performed. Recent advances in camera technology have greatly reduced this bottleneck, with the detection efficiency of direct electron detectors being especially well suited to the technique. However even the fastest frame driven pixelated detectors still significantly limit the scan speed which can be used in 4D STEM, making the resulting data susceptible to drift and hampering its use for low dose beam sensitive applications. Here we report the development of the use of an event driven Timepix3 direct electron camera that allows us to overcome this bottleneck and achieve 4D STEM dwell times down to 100 ns; orders of magnitude faster than what has been possible with frame based readout. We characterise the detector for different acceleration voltages and show that the method is especially well suited for low dose imaging and promises rich datasets without compromising dwell time when compared to conventional STEM imaging.

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“…thickness variation, it fails to present such information in the result. This is due to the band-pass nature of the method (Yang et al, 2015;O'Leary et al, 2021). Long range features are build with low frequency components, and thus for reconstruction methods that filter out, or cannot utilize signals that fall in the low frequency end, these features are lost.…”

Section: Contrast Analysismentioning

confidence: 99%

Phase Object Reconstruction for 4D-STEM using Deep Learning

Friedrich¹,

Yu²,

Verbeeck³

et al. 2022

Preprint

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In this study we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex electron wave function is recovered for a convergent beam electron diffraction pattern (CBED) using a convolutional neural network (CNN). Subsequently a corresponding patch of the phase object is recovered using the phase object approximation (POA). Repeating this for each scan position in a 4D-STEM dataset and combining the patches by complex summation yields the full phase object. Each patch is recovered from a kernel of 3x3 adjacent CBEDs only, which eliminates common, large memory requirements and enables live processing during an experiment. The machine learning pipeline, data generation and the reconstruction algorithm are presented. We demonstrate that the CNN can retrieve phase information beyond the aperture angle, enabling super-resolution imaging. The image contrast formation is evaluated showing a dependence on thickness and atomic column type. Columns containing light and heavy elements can be imaged simultaneously and are distinguishable. The combination of super-resolution, good noise robustness and intuitive image contrast characteristics makes the approach unique among live imaging methods in 4D-STEM.

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Contrast transfer and noise considerations in focused-probe electron ptychography

Cited by 46 publications

References 51 publications

Real Time Integration Centre of Mass (riCOM) Reconstruction for 4D-STEM

Real Time Integration Centre of Mass (riCOM) Reconstruction for 4D-STEM

Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwell time and faster scans for high precision and low dose applications

Phase Object Reconstruction for 4D-STEM using Deep Learning

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