14 research outputs found
Multibeam Electron Diffraction
One of the primary uses for transmission electron microscopy (TEM) is to
measure diffraction pattern images in order to determine a crystal structure
and orientation. In nanobeam electron diffraction (NBED) we scan a moderately
converged electron probe over the sample to acquire thousands or even millions
of sequential diffraction images, a technique that is especially appropriate
for polycrystalline samples. However, due to the large Ewald sphere of TEM,
excitation of Bragg peaks can be extremely sensitive to sample tilt, varying
strongly for even a few degrees of sample tilt for crystalline samples. In this
paper, we present multibeam electron diffraction (MBED), where multiple probe
forming apertures are used to create mutiple STEM probes, all of which interact
with the sample simultaneously. We detail designs for MBED experiments, and a
method for using a focused ion beam (FIB) to produce MBED apertures. We show
the efficacy of the MBED technique for crystalline orientation mapping using
both simulations and proof-of-principle experiments. We also show how the
angular information in MBED can be used to perform 3D tomographic
reconstruction of samples without needing to tilt or scan the sample multiple
times. Finally, we also discuss future opportunities for the MBED method.Comment: 14 pages, 6 figure
pynucastro 2.1: an update on the development of a python library for nuclear astrophysics
pynucastro is a python library that provides visualization and analyze
techniques to classify, construct, and evaluate nuclear reaction rates and
networks. It provides tools that allow users to determine the importance of
each rate in the network, based on a specified list of thermodynamic
properties. Additionally, pynucastro can output a network in C++ or python for
use in simulation codes, include the AMReX-Astrophysics simulation suite. We
describe the changes in pynucastro since the last major release, including new
capabilities that allow users to generate reduced networks and thermodynamic
tables for conditions in nuclear statistical equilibrium
py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets
Scanning transmission electron microscopy (STEM) allows for imaging,
diffraction, and spectroscopy of materials on length scales ranging from
microns to atoms. By using a high-speed, direct electron detector, it is now
possible to record a full 2D image of the diffracted electron beam at each
probe position, typically a 2D grid of probe positions. These 4D-STEM datasets
are rich in information, including signatures of the local structure,
orientation, deformation, electromagnetic fields and other sample-dependent
properties. However, extracting this information requires complex analysis
pipelines, from data wrangling to calibration to analysis to visualization, all
while maintaining robustness against imaging distortions and artifacts. In this
paper, we present py4DSTEM, an analysis toolkit for measuring material
properties from 4D-STEM datasets, written in the Python language and released
with an open source license. We describe the algorithmic steps for dataset
calibration and various 4D-STEM property measurements in detail, and present
results from several experimental datasets. We have also implemented a simple
and universal file format appropriate for electron microscopy data in py4DSTEM,
which uses the open source HDF5 standard. We hope this tool will benefit the
research community, helps to move the developing standards for data and
computational methods in electron microscopy, and invite the community to
contribute to this ongoing, fully open-source project
Prismatic 2.0 - Simulation software for scanning and high resolution transmission electron microscopy (STEM and HRTEM).
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Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets
Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is "out-of-distribution" from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms
Atomic Resolution Convergent Beam Electron Diffraction Analysis Using Convolutional Neural Networks
A Fast Algorithm for Scanning Transmission Electron Microscopy (STEM) Imaging and 4D-STEM Diffraction Simulations
Scanning transmission electron microscopy (STEM) is an extremely versatile
method for studying materials on the atomic scale. Many STEM experiments are
supported or validated with electron scattering simulations. However, using the
conventional multislice algorithm to perform these simulations can require
extremely large calculation times, particularly for experiments with millions
of probe positions as each probe position must be simulated independently.
Recently, the PRISM algorithm was developed to reduce calculation times for
large STEM simulations. Here, we introduce a new method for STEM simulation:
partitioning of the STEM probe into "beamlets," given by a natural neighbor
interpolation of the parent beams. This idea is compatible with PRISM
simulations and can lead to even larger improvements in simulation time, as
well requiring significantly less computer RAM. We have performed various
simulations to demonstrate the advantages and disadvantages of partitioned
PRISM STEM simulations. We find that this new algorithm is particularly useful
for 4D-STEM simulations of large fields of view. We also provide a reference
implementation of the multislice, PRISM and partitioned PRISM algorithms
A Fast Algorithm for Scanning Transmission Electron Microscopy Imaging and 4D-STEM Diffraction Simulations.
Scanning transmission electron microscopy (STEM) is an extremely versatile method for studying materials on the atomic scale. Many STEM experiments are supported or validated with electron scattering simulations. However, using the conventional multislice algorithm to perform these simulations can require extremely large calculation times, particularly for experiments with millions of probe positions as each probe position must be simulated independently. Recently, the plane-wave reciprocal-space interpolated scattering matrix (PRISM) algorithm was developed to reduce calculation times for large STEM simulations. Here, we introduce a new method for STEM simulation: partitioning of the STEM probe into “beamlets,” given by a natural neighbor interpolation of the parent beams. This idea is compatible with PRISM simulations and can lead to even larger improvements in simulation time, as well requiring significantly less computer random access memory (RAM). We have performed various simulations to demonstrate the advantages and disadvantages of partitioned PRISM STEM simulations. We find that this new algorithm is particularly useful for 4D-STEM simulations of large fields of view. We also provide a reference implementation of the multislice, PRISM, and partitioned PRISM algorithms
Recommended from our members
Multibeam Electron Diffraction.
One of the primary uses for transmission electron microscopy (TEM) is to measure diffraction pattern images in order to determine a crystal structure and orientation. In nanobeam electron diffraction (NBED), we scan a moderately converged electron probe over the sample to acquire thousands or even millions of sequential diffraction images, a technique that is especially appropriate for polycrystalline samples. However, due to the large Ewald sphere of TEM, excitation of Bragg peaks can be extremely sensitive to sample tilt, varying strongly for even a few degrees of sample tilt for crystalline samples. In this paper, we present multibeam electron diffraction (MBED), where multiple probe-forming apertures are used to create multiple scanning transmission electron microscopy (STEM) probes, all of which interact with the sample simultaneously. We detail designs for MBED experiments, and a method for using a focused ion beam to produce MBED apertures. We show the efficacy of the MBED technique for crystalline orientation mapping using both simulations and proof-of-principle experiments. We also show how the angular information in MBED can be used to perform 3D tomographic reconstruction of samples without needing to tilt or scan the sample multiple times. Finally, we also discuss future opportunities for the MBED method