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Register a new userFast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation
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Fast pixelated detectors incorporating direct electron detection (DED) technology are increasingly being regarded as universal detectors for scanning transmission electron microscopy (STEM), capable of imaging under multiple modes of operation. However, several issues remain around the post acquisition processing and visualisation of the often very large multidimensional STEM datasets produced by them. We discuss these issues and present open source software libraries to enable efficient processing and visualisation of such datasets. Throughout, we provide examples of the analysis methodologies presented, utilising data from a 256$times$256 pixel Medipix3 hybrid DED detector, with a particular focus on the STEM characterisation of the structural properties of materials. These include the techniques of virtual detector imaging; higher order Laue zone analysis; nanobeam electron diffraction; and scanning precession electron diffraction. In the latter, we demonstrate nanoscale lattice parameter mapping with a fractional precision $le 6times10^{-4}$ (0.06%).
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The use of fast pixelated detectors and direct electron detection technology is revolutionising many aspects of scanning transmission electron microscopy (STEM). The widespread adoption of these new technologies is impeded by the technical challenges associated with them. These include issues related to hardware control, and the acquisition, real-time processing and visualisation, and storage of data from such detectors. We discuss these problems and present software solutions for them, with a view to making the benefits of new detectors in the context of STEM more accessible. Throughout, we provide examples of the application of the technologies presented, using data from a Medipix3 direct electron detector. Most of our software is available under an open source licence, permitting transparency of the implemented algorithms, and allowing the community to freely use and further improve upon them.
Thin film oxides are a source of endless fascination for the materials scientist. These materials are highly flexible, can be integrated into almost limitless combinations, and exhibit many useful functionalities for device applications. While precision synthesis techniques, such as molecular beam epitaxy (MBE) and pulsed laser deposition (PLD), provide a high degree of control over these systems, there remains a disconnect between ideal and realized materials. Because thin films adopt structures and chemistries distinct from their bulk counterparts, it is often difficult to predict what properties will emerge. The complex energy landscape of the synthesis process is also strongly influenced by non-equilibrium growth conditions imposed by the substrate, as well as the kinetics of thin film crystallization and fluctuations in process variables, all of which can lead to significant deviations from targeted outcomes. High-resolution structural and chemical characterization techniques, as described in this volume, are needed to verify growth models, bound theoretical calculations, and guide materials design. While many characterization options exist, most are spatially-averaged or indirect, providing only partial insight into the complex behavior of these systems. Over the past several decades, scanning transmission electron microscopy (STEM) has become a cornerstone of oxide heterostructure characterization owing to its ability to simultaneously resolve structure, chemistry, and defects at the highest spatial resolution. STEM methods are an essential complement to averaged scattering techniques, offering a direct picture of resulting materials that can inform and refine the growth process to achieve targeted properties. There is arguably no other technique that can provide such a broad array of information at the atomic-scale, all within a single experimental session.
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.
Electron tomography in materials science has flourished with the demand to characterize nanoscale materials in three dimensions (3D). Access to experimental data is vital for developing and validating reconstruction methods that improve resolution and reduce radiation dose requirements. This work presents five high-quality scanning transmission electron microscope (STEM) tomography datasets in order to address the critical need for open access data in this field. The datasets represent the current limits of experimental technique, are of high quality, and contain materials with structural complexity. Included are tomographic series of a hyperbranched Co2P nanocrystal, platinum nanoparticles on a carbon nanofibre imaged over the complete 180{deg} tilt range, a platinum nanoparticle and a tungsten needle both imaged at atomic resolution by equal slope tomography, and a through-focal tilt series of PtCu nanoparticles. A volumetric reconstruction from every dataset is provided for comparison and development of post-processing and visualization techniques. Researchers interested in creating novel data processing and reconstruction algorithms will now have access to state of the art experimental test data.
Due to its ultrahigh electron transmissivity in a wide electron energy range, molecular impermeability, high electrical conductivity and excellent mechanical stiffness the suspended graphene membranes appear to be a nearly ideal window material for in situ (in vivo) environmental electron microscopy of nano- and mesoscopic objects (including bio-medical samples) immersed in liquids and/or in dense gaseous media. In this communication, taking advantage of little modification of the graphene transfer protocol on to metallic and SiN supporting orifices, the reusable environmental cells with exchangeable graphene windows have been designed. Using colloidal gold nanoparticles (50 nm) dispersed in water as model objects for scanning electron microscopy in liquids, the different imaging conditions through graphene membrane have been tested. The limiting factors for electron microscopy in liquids such as electron beam induced water radiolysis and damage of graphene membrane at high electron doses were discussed.
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