Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFactors limiting quantitative phase retrieval in atomic-resolution differential phase contrast scanning transmission electron microscopy using a segmented detector
77
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Quantitative differential phase contrast imaging of materials in atomic-resolution scanning transmission electron microscopy using segmented detectors is limited by various factors, including coherent and incoherent aberrations, detector positioning and uniformity, and scan-distortion. By comparing experimental case studies of monolayer and few-layer graphene with image simulations, we explore which parameters require the most precise characterisation for reliable and quantitative interpretation of the reconstructed phases. Coherent and incoherent lens aberrations are found to have the most significant impact. For images over a large field of view, the impact of noise and non-periodic boundary conditions are appreciable, but in this case study have less of an impact than artefacts introduced by beam deflections coupling to beam scanning (imperfect tilt-shift purity).
rate research
Read More
In differential phase contrast scanning transmission electron microscopy (DPC-STEM), variability in dynamical diffraction resulting from changes in sample thickness and local crystal orientation (due to sample bending) can produce contrast comparable to that arising from the long-range electromagnetic fields probed by this technique. Through simulation we explore the scale of these dynamical diffraction artefacts and introduce a metric for the magnitude of their confounding contribution to the contrast. We show that precession over an angular range of a few milliradian can suppress this confounding contrast by one-to-two orders of magnitude. Our exploration centres around a case study of GaAs near the [011] zone-axis orientation using a probe-forming aperture semiangle on the order of 0.1 mrad at 300 keV, but the trends found and methodology used are expected to apply more generally.
Single atoms can be considered as basic objects for electron microscopy to test the microscope performance and basic concepts for modeling of image contrast. In this work high-resolution transmission electron microscopy was applied to image single platinum atoms in an aberration-corrected transmission electron microscope. The atoms are deposited on a self-assembled monolayer substrate which induces only negligible contrast. Single-atom contrast simulations were performed on the basis of Weickenmeier-Kohl and Doyle-Turner scattering factors. Experimental and simulated intensities are in full agreement on an absolute scale.
The rigid-intensity-shift model of differential phase contrast scanning transmission electron microscopy (DPC-STEM) imaging assumes that the phase gradient imposed on the probe by the sample causes the diffraction pattern intensity to shift rigidly by an amount proportional to that phase gradient. This behaviour is seldom realised exactly in practice. Through a combination of experimental results, analytical modelling and numerical calculations, we explore the breakdown of the rigid-intensity-shift behaviour and how this depends on the magnitude of the phase gradient and the relative scale of features in the phase profile and the probe size. We present guidelines as to when the rigid-intensity-shift model can be applied for quantitative phase reconstruction using segmented detectors, and propose probe-shaping strategies to further improve the accuracy.
We present differential phase-contrast optical coherence tomography (DPC-OCT) with two transversally separated probing beams to sense phase gradients in various directions by employing a rotatable Wollaston prism. In combination with a two-dimensional mathe- matical reconstruction algorithm based on a regularized shape from shading (SfS) method accurate quantitative phase maps can be determined from a set of two orthogonal en-face DPC-OCT images, as exemplified on various technical samples.
Scanning transmission electron microscopy (STEM) is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of active learning methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO$_3$ matrix, ferroelectric domains in BiFeO$_3$, and topological defects in graphene. The code developed in this manuscript are open sourced and will be released at github.com/creangnc/AE_Workflows.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
