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Subsampling Methods for Fast Electron Backscattered Diffraction Analysis for SEM

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 467-469页

作者： Broad, Zoë Nicholls, Daniel Wells, Jack Moshtaghpour, Amirafshar Robinson, Alex W. Masters, Robert Hughes, Louise Browning, Nigel D. Department of Mechanical Materials and Aerospace Engineering University of Liverpool United Kingdom Distributed Algorithms Centre for Doctoral Training University of Liverpool Liverpool United Kingdom Correlated Imaging Group Rosalind Franklin Institute Harwell Science and Innovation Campus Didcot United Kingdom Oxford Instruments Nanoanalysis High Wycombe United Kingdom

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A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy 48

A Targeted Sampling Strategy for Compressive Cryo Focused Io...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Nicholls, D. Wells, J. Robinson, A.W. Moshtaghpour, A. Kobylynska, M. Fleck, R.A. Kirkland, A.I. Browning, N.D. University of Liverpool Distributed Algorithms Centre for Doctoral Training United Kingdom King's College London Centre for Ultrastructural Imaging London United Kingdom Harwell Science and Innovation Campus Correlated Imaging Group Rosalind Franklin Institute Didcot United Kingdom University of Oxford Department of Materials United Kingdom University of Liverpool Department of Mechanical Materials and Aerospace Engineering United Kingdom Pacific Northwest National Laboratory Physical and Computational Science Richland United States Sivananthan Laboratories 590 Territorial Drive BolingbrookIL United States

ISBN: (纸本)9781728163277

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 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. © 2023 IEEE.

关键词： Beta-process factor analysis Compressive sensing Focused ion beam Scanning electron microscopy

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Subsampling Methods for Fast Electron Backscattered Diffraction Analysis

arXiv

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arXiv 2023年

作者： Broad, Zoë Nicholls, Daniel Wells, Jack Robinson, Alex W. Moshtaghpour, Amirafshar Masters, Robert Hughes, Louise Browning, Nigel D. Department of Mechanical Materials and Aerospace Engineering University of Liverpool United Kingdom Distributed Algorithms Centre for Doctoral Training University of Liverpool Liverpool United Kingdom Correlated Imaging Group Rosalind Franklin Institute Harwell Science and Innovation Campus Didcot United Kingdom Oxford Instruments Nanoanalysis High Wycombe United Kingdom

Despite advancements in electron backscatter diffraction (EBSD) detector speeds, the acquisition rates of 4-Dimensional (4D) EBSD data, i.e., a collection of 2-dimensional (2D) diffraction maps for every position of a convergent electron probe on the sample, is limited by the capacity of the detector. Such 4D data enables computation of, e.g., band contrast and Inverse Pole Figure (IPF) maps, used for material characterisation. In this work we propose a fast acquisition method of EBSD data through subsampling 2-D probe positions and inpainting. We investigate reconstruction of both band contrast and IPF maps using an unsupervised Bayesian dictionary learning approach, i.e., Beta process factor analysis. Numerical simulations achieve high quality reconstructed images from 10% subsampled data. Copyright © 2023, The Authors. All rights reserved.

关键词： Probes

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Compressive Electron Backscatter Diffraction Imaging

arXiv

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arXiv 2024年

作者： Broad, Zoë Robinson, Alex W. Wells, Jack Nicholls, Daniel Moshtaghpour, Amirafshar Kirkland, Angus I. Browning, Nigel D. Department of Mechanical Materials and Aerospace Engineering University of Liverpool United Kingdom SenseAI Innovations Ltd. Liverpool United Kingdom Distributed Algorithms Centre for Doctoral Training University of Liverpool Liverpool United Kingdom Correlated Imaging Group Rosalind Franklin Institute Harwell Science Innovation Campus Didcot United Kingdom Department of Materials University of Oxford Oxford United Kingdom

Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete dataset. The missing probe locations (or pixels in the image) are recovered via an inpainting process using a dictionary-learning based method called beta-process factor analysis (BPFA). To investigate the robustness of both our inpainting method and Hough-based indexing, we simulate subsampled and noisy EBSD datasets from a real fully sampled Ni-superalloy dataset for different subsampling ratios of probe positions using both Gaussian and Poisson noise models. We find that zero solution pixel detection (inpainting un-indexed pixels) enables higher quality reconstructions to be obtained. Numerical tests confirm high quality reconstruction of band contrast and inverse pole figure maps from only 10% of the probe positions, with the potential to reduce this to 5% if only inverse pole figure maps are needed. These results show the potential application of this method in EBSD, allowing for faster analysis and extending the use of this technique to beam sensitive materials. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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Compressive Cryo FIB-SEM Tomography

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 526-527页

作者： Nicholls, Daniel Wells, Jack Robinson, Alex W. Moshtaghpour, Amirafshar Kobylynska, Maryna Fleck, Roland A. Kirkland, Angus I. Mehdi, B Layla Browning, Nigel D. Department of Mechanical Materials and Aerospace Engineering University of Liverpool United Kingdom Distributed Algorithms Centre for Doctoral Training University of Liverpool Liverpool United Kingdom Correlated Imaging Group Rosalind Franklin Institute Harwell Science and Innovation Campus Didcot United Kingdom Centre for Ultrastructural Imaging King's College London London United Kingdom Department of Materials University of Oxford United Kingdom Physical and Computational Science Directorate Pacific Northwest National Laboratory Richland WA United States Harwell Science and Innovation Campus Didcot United Kingdom Sivananthan Laboratories 590 Territorial Drive Bolingbrook IL United States

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Double Deep Q Networks for Sensor Management in Space Situational Awareness

arXiv

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arXiv 2022年

作者： Oakes, Benedict Richards, Dominic Barr, Jordi Ralph, Jason CDT in Distributed Algorithms University of Liverpool Liverpool United Kingdom Artificial Intelligence Group STFC Hartree Centre Daresbury United Kingdom Cyber and Information Systems Division Defence Science and Technology Laboratory United Kingdom Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

We present a novel Double Deep Q Network (DDQN) application to a sensor management problem in space situational awareness (SSA). Frequent launches of satellites into Earth orbit pose a significant sensor management challenge, whereby a limited number of sensors are required to detect and track an increasing number of objects. In this paper, we demonstrate the use of reinforcement learning to develop a sensor management policy for SSA. We simulate a controllable Earth-based telescope, which is trained to maximise the number of satellites tracked using an extended Kalman filter. The estimated state covariance matrices for satellites observed under the DDQN policy are greatly reduced compared to those generated by an alternate (random) policy. This work provides the basis for further advancements and motivates the use of reinforcement learning for SSA. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

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SenseAI: Real-Time Inpainting for Electron Microscopy

arXiv

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arXiv 2023年

作者： Wells, Jack Moshtaghpour, Amirafshar Nicholls, Daniel Robinson, Alex W. Zheng, Yalin Castagna, Jony Browning, Nigel D. Distributed Algorithms Centre for Doctoral Training University of Liverpool Liverpool United Kingdom Department of Mechanical Materials and Aerospace Engineering University of Liverpool United Kingdom Correlated Imaging Group Rosalind Franklin Institute Harwell Science and Innovation Campus Didcot United Kingdom Physical and Computational Science Directorate Pacific Northwest National Laboratory Richland United States Sivananthan Laboratories 590 Territorial Drive BolingbrookIL United States

Despite their proven success and broad applicability to Electron Microscopy (EM) data, joint dictionary-learning and sparse-coding based inpainting algorithms have so far remained impractical for real-time usage with an Electron Microscope. For many EM applications, the reconstruction time for a single"frame" is orders of magnitude longer than the data acquisition time, making it impossible to perform exclusively subsampled acquisition. This limitation has led to the development of SenseAI, a C++/CUDA library capable of extremely efficient dictionary-based inpainting. SenseAI provides N-dimensional dictionary learning, live reconstructions, dictionary transfer and visualization, as well as real-time plotting of statistics, parameters, and image quality metrics. Copyright © 2023, The Authors. All rights reserved.

关键词： Electron microscopy

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

A Targeted Sampling Strategy for Compressive Cryo Focused Io...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： D. Nicholls J. Wells A. W. Robinson A. Moshtaghpour M. Kobylynska R. A. Fleck A. I. Kirkland N. D. Browning Department of Mechanical Materials and Aerospace Engineering University of Liverpool UK Distributed Algorithms Centre for Doctoral Training University of Liverpool UK Correlated Imaging Group Rosalind Franklin Institute Harwell Science and Innovation Campus Didcot UK Centre for Ultrastructural Imaging King’s College London London UK Department of Materials University of Oxford UK Physical and Computational Science Directorate Pacific Northwest National Laboratory Richland USA Sivananthan Laboratories Bolingbrook IL USA

关键词： Scanning electron microscopy Microscopy Machine learning Signal processing Nanoscale devices Acoustic beams Sensors

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In silico Ptychography of Lithium-ion Cathode Materials from Subsampled 4-D STEM Data

arXiv

引用

arXiv 2023年

作者： Robinson, Alex W. Moshtaghpour, Amirafshar Wells, Jack Nicholls, Daniel Broad, Zoë Kirkland, Angus I. Mehdi, Beata L. Browning, Nigel D. Department of Mechanical Materials and Aerospace Engineering University of Liverpool United Kingdom Distributed Algorithms Centre for Doctoral Training University of Liverpool Liverpool United Kingdom Correlated Imaging Group Rosalind Franklin Institute Harwell Science and Innovation Campus Didcot United Kingdom Department of Materials University of Oxford United Kingdom Physical and Computational Science Directorate Pacific Northwest National Laboratory Richland United States Sivananthan Laboratories 590 Territorial Drive BolingbrookIL United States

High quality scanning transmission electron microscopy (STEM) data acquisition and analysis has become increasingly important due to the commercial demand for investigating the properties of complex materials such as battery cathodes;however, multidimensional techniques (such as 4-D STEM) which can improve resolution and sample information are ultimately limited by the beam-damage properties of the materials or the signal-to-noise ratio of the result. subsampling offers a solution to this problem by retaining high signal, but distributing the dose across the sample such that the damage can be reduced. It is for these reasons that we propose a method of subsampling for 4-D STEM, which can take advantage of the redundancy within said data to recover functionally identical results to the ground truth. We apply these ideas to a simulated 4-D STEM data set of a LiMnO2 sample and we obtained high quality reconstruction of phase images using 12.5% subsampling. © 2023, CC BY.

关键词： Data acquisition

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Simultaneous High-Speed and Low-Dose 4-D STEM Using Compressive Sensing Techniques

arXiv

引用

arXiv 2023年

作者： Robinson, Alex W. Moshtaghpour, Amirafshar Wells, Jack Nicholls, Daniel Chi, Miaofang MacLaren, Ian Kirkland, Angus I. Browning, Nigel D. Department of Mechanical Materials and Aerospace Engineering University of Liverpool United Kingdom SenseAI Innovations Ltd. University of Liverpool Liverpool United Kingdom Correlated Imaging Group Rosalind Franklin Institute Harwell Science and Innovation Campus Didcot United Kingdom Distributed Algorithms Centre for Doctoral Training University of Liverpool United Kingdom Chemical Science Division Centre for Nanophase Materials Sciences Oak Ridge National Laboratory Tennessee United States School of Physics and Astronomy University of Glasgow Glasgow United Kingdom Department of Materials University of Oxford United Kingdom

Here we show that compressive sensing allow 4-dimensional (4-D) STEM data to be obtained and accurately reconstructed with both high-speed and low fluence. The methodology needed to achieve these results compared to conventional 4-D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns, and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide data-set shows that it is possible to recover over 25dB peak signal-to-noise in the recovered phase using 0.3% of the total data. © 2023, CC BY.

关键词： Compressed sensing

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