SIM-STEM Lab: Incorporating Compressed Sensing Theory for Fast STEM Simulation

Robinson, Alex W.; Nicholls, Daniel; Wells, Jack; Moshtaghpour, Amirafshar; Kirkland, Angus I.; Browning, Nigel D.

doi:10.1016/j.ultramic.2022.113625

Cited by 10 publications

(12 citation statements)

References 46 publications

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“…Spatial subsampling [30][31][32][33][34][35][36][37][38][39] has previously been applied to experimental STEM images [32][33][34] and has shown a reduction in beam induced damage, faster acquisition speed, and reduced drift and image distortion without loss of signal at each measured probe location (i.e. the same probe dwell time and current).…”

Section: Methods: From Random Sampling To Targeted Sampling In Stem S...mentioning

confidence: 99%

“…Therefore, we propose that this file could also be used as a method to form a targeted sampling mask which prioritises sampling at atom locations (given we know this from the file provided), as opposed to a purely random approach (as in Refs. 30,31 ). However, the sampling is not purely targeted, but also includes some bias 𝑅, which allows any location to be sampled at random.…”

Section: Methods Of Targeted Samplingmentioning

confidence: 99%

“…Therefore, for the purpose of speeding up simulations, it is better to limit the number of configurations to the point where the improvement begins to diminish. 30,31 As an example, consider sampling at a location where there is no atom. Here the number of configurations used makes an insignificant contribution to the intensity of the pixel at that location, and therefore to continue sampling this position would be time inefficient.…”

Section: Applying Targeted Sampling To the Frozen Phonon Modelmentioning

confidence: 99%

“…where (𝑎 × 𝑏) is the size of the orthogonal scan area, 𝑀 𝑥 × 𝑀 𝑦 is the scan dimension (pixels), 𝛼 and 𝜆 are the probe convergence semiangle and electron wavelength respectively. 29 In our previous work, 30,31 it was demonstrated that it is possible to effectively compress a simulation through spatial, reciprocal space, and FPC subsampling (Figure 1) to ultimately reduce the computation time and still obtain functionally identical results. A spatially compressed simulation (i.e.…”

mentioning

confidence: 99%

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Towards real‐time STEM simulations through targeted subsampling strategies

Robinson

Wells

Nicholls

et al. 2023

Journal of Microscopy

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Scanning transmission electron microscopy images can be complex to interpret on the atomic scale as the contrast is sensitive to multiple factors such as sample thickness, composition, defects and aberrations. Simulations are commonly used to validate or interpret real experimental images, but they come at a cost of either long computation times or specialist hardware such as graphics processing units. Recent works in compressive sensing for experimental STEM images have shown that it is possible to significantly reduce the amount of acquired signal and still recover the full image without significant loss of image quality, and therefore it is proposed here that similar methods can be applied to STEM simulations. In this paper, we demonstrate a method that can significantly increase the efficiency of STEM simulations through a targeted sampling strategy, along with a new approach to independently subsample each frozen phonon layer. We show the effectiveness of this method by simulating a SrTiO3 grain boundary and monolayer 2H‐MoS2 containing a sulphur vacancy using the abTEM software. We also show how this method is not limited to only traditional multislice methods, but also increases the speed of the PRISM simulation method. Furthermore, we discuss the possibility for STEM simulations to seed the acquisition of real data, to potentially lead the way to self‐driving (correcting) STEM.

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Section: Methods: From Random Sampling To Targeted Sampling In Stem S...mentioning

confidence: 99%

Section: Methods Of Targeted Samplingmentioning

confidence: 99%

Section: Applying Targeted Sampling To the Frozen Phonon Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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Towards real‐time STEM simulations through targeted subsampling strategies

Robinson

Wells

Nicholls

et al. 2023

Journal of Microscopy

Self Cite

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“…However, in this paper we adopt the Targeted Sampling (TS) strategy introduced in [17] for simulating electron microscopy images. For sampling layer l ∈ {1, • • • , N 3 }, given the number of measurements M and the TS parameter ρ ∈ [0, 1], we select M t := ρM indices (i.e., targeted part) in Ω l , w.r.t the probability distribution p l on {1, • • • , N } (without replacement) and select M r = M − M t indices uniformly at random (i.e., random part).…”

Section: Acquisition Modelmentioning

confidence: 99%

A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy

Nicholls¹,

Wells²,

Robinson³

et al. 2022

Preprint

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Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables three-dimensional and nanoscale imaging of biological specimens via a slice and view mechanism. The FIB-SEM experiments are, however, limited by a slow (typically, several hours) acquisition process and the high electron doses imposed on the beam sensitive specimen can cause damage. In this work, we present a compressive sensing variant of cryo FIB-SEM capable of reducing the operational electron dose and increasing speed. We propose two Targeted Sampling (TS) strategies that leverage the reconstructed image of the previous sample layer as a prior for designing the next subsampling mask. Our image recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta Process Factor Analysis (BPFA). This method is experimentally viable due to our ultra-fast GPU-based implementation of BPFA. Simulations on artificial compressive FIB-SEM measurements validate the success of proposed methods: the operational electron dose can be reduced by up to 20 times. These methods have large implications for the cryo FIB-SEM community, in which the imaging of beam sensitive biological materials without beam damage is crucial.

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