Computation in electron microscopy

Kirkland, Earl J.

doi:10.1107/s205327331501757x

Cited by 34 publications

(25 citation statements)

References 243 publications

(249 reference statements)

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“…To understand the depth dependence of the plane-view images, multiple-scattering calculations of electron beam propagation through the model skyrmion bubble (Fig. 2) were performed using the multislice method 25 to produce the simulated ADF ( Fig. 3f) and p (Fig.…”

Section: ∫ ∫mentioning

confidence: 99%

Observation of room-temperature polar skyrmions

Das

Tang

Hong

et al. 2019

Nature

544

417

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Section: ∫ ∫mentioning

confidence: 99%

Observation of room-temperature polar skyrmions

Das

Tang

Hong

et al. 2019

Nature

544

417

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“…Many STEM studies such as high precision 2D measurements [26][27][28], 3D atomic electron tomography [29,30], and others [31], make use of image simulations of many thousands of STEM probe positions. This requires long computation times, even with modern implementations of the multislice method [25,[32][33][34][35][36][37]. It is therefore desirable to develop an electron scattering simulation algorithm that shares the calculation burden between STEM probe positions in a more efficient manner than multislice simulation.…”

Section: Introductionmentioning

confidence: 99%

A fast image simulation algorithm for scanning transmission electron microscopy

Ophus

2017

Adv Struct Chem Imag

155

148

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Image simulation for scanning transmission electron microscopy at atomic resolution for samples with realistic dimensions can require very large computation times using existing simulation algorithms. We present a new algorithm named PRISM that combines features of the two most commonly used algorithms, namely the Bloch wave and multislice methods. PRISM uses a Fourier interpolation factor f that has typical values of 4–20 for atomic resolution simulations. We show that in many cases PRISM can provide a speedup that scales with f 4 compared to multislice simulations, with a negligible loss of accuracy. We demonstrate the usefulness of this method with large-scale scanning transmission electron microscopy image simulations of a crystalline nanoparticle on an amorphous carbon substrate.

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“…The simplest is the convolution method, an incoherent linear image model that convolves the probe point-spread function with simple atomic potentials for the specimen [14]. This method assumes that there is no dynamic scattering and no interference between scattered and unscattered electrons [15]. Although the convolution method allows images to be computed very quickly, it is only accurate for very thin samples, and so has seen limited use.…”

Section: Introductionmentioning

confidence: 99%

Fast approximate STEM image simulations from a machine learning model

Combs

Maldonis

Feng

et al. 2019

Adv Struct Chem Imag

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Accurate quantum mechanical scanning transmission electron microscopy image simulation methods such as the multislice method require computation times that are too large to use in applications in high-resolution materials imaging that require very large numbers of simulated images. However, higher-speed simulation methods based on linear imaging models, such as the convolution method, are often not accurate enough for use in these applications. We present a method that generates an image from the convolution of an object function and the probe intensity, and then uses a multivariate polynomial fit to a dataset of corresponding multislice and convolution images to correct it. We develop and validate this method using simulated images of Pt and Pt-Mo nanoparticles and find that for these systems, once the polynomial is fit, the method runs about six orders of magnitude faster than parallelized CPU implementations of the multislice method while achieving a 1 − R 2 error of 0.010-0.015 and root-mean-square error/ standard deviation of dataset being predicted of about 0.1 when compared to full multislice simulations.

show abstract

Computation in electron microscopy

Cited by 34 publications

References 243 publications

Observation of room-temperature polar skyrmions

Observation of room-temperature polar skyrmions

A fast image simulation algorithm for scanning transmission electron microscopy

Fast approximate STEM image simulations from a machine learning model

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