arXiv subjects

Andrew R. Lupini

Publications and source records attributed to Andrew R. Lupini.

At least 37 records · Page 2Linked to original sources

arXiv2023

Automatic and quantitative measurement of spectrometer aberrations

The performance of electron energy-loss spectrometers can often be limited by their electron-optical aberrations. Due to recent developments in high energy-resolution and momentum-resolved electron energy loss spectroscopy (EELS), there is renewed interest in optimizing the performance of such spectrometers. For example, the "ω-q" mode of momentum-resolved EELS, which uses a small convergence angle and requires aligning diffraction spots with a slot aperture, presents a challenge for realigning the spectrometer after adjusting the projection lenses. Automated and robust alignment can greatly benefit such a process. The first step towards this goal is automatic and quantitative measurement of spectrometer aberrations. Here we demonstrate the measurement of geometric aberrations and distortions in EELS within a monochromated scanning transmission electron microscope(STEM). To better understand the results, we present a wave mechanical simulation of the experiment. Using the measured aberration and distortion coefficients as inputs to the simulation, we find a good match between the simulation and experiment, verifying the approach used in the simulation, allowing us to assess the accuracy of the measurements. Understanding the errors and inaccuracies in the procedure can guide further progress in aberration measurement and correction for new spectrometer developments.

cond-mat.mtrl-sci↗

arXiv2023

Revealing intrinsic vortex-core states in Fe-based superconductors through machine-learning-driven discovery

Electronic states within superconducting vortices hold crucial information about paring mechanisms and topology. While scanning tunneling microscopy/spectroscopy(STM/S) can image the vortices, it is difficult to isolate the intrinsic electronic states from extrinsic effects like subsurface defects and disorders. We combine STM/S with unsupervised machine learning to develop a method for screening out the vortices pinned by embedded disorder in Fe-based superconductors. The approach provides an unbiased way to reveal intrinsic vortex-core states and may address puzzles on Majorana zero modes.

cond-mat.supr-con↗

arXiv2023

Top-down fabrication of atomic patterns in twisted bilayer graphene

Atomic-scale engineering typically involves bottom-up approaches, leveraging parameters such as temperature, partial pressures, and chemical affinity to promote spontaneous arrangement of atoms. These parameters are applied globally, resulting in atomic scale features scattered probabilistically throughout the material. In a top-down approach, different regions of the material are exposed to different parameters resulting in structural changes varying on the scale of the resolution. In this work, we combine the application of global and local parameters in an aberration corrected scanning transmission electron microscope (STEM) to demonstrate atomic scale precision patterning of atoms in twisted bilayer graphene. The focused electron beam is used to define attachment points for foreign atoms through the controlled ejection of carbon atoms from the graphene lattice. The sample environment is staged with nearby source materials, such that the sample temperature can induce migration of the source atoms across the sample surface. Under these conditions, the electron-beam (top-down) enables carbon atoms in the graphene to be replaced spontaneously by diffusing adatoms (bottom-up). Using image-based feedback-control, arbitrary patterns of atoms and atom clusters are attached to the twisted bilayer graphene with limited human interaction. The role of substrate temperature on adatom and vacancy diffusion is explored by first-principles simulations.

cond-mat.mtrl-sci↗

arXiv2022

Direct Imaging of Electron Orbitals with a Scanning Transmission Electron Microscope

Recent studies of secondary electron (SE) emission in scanning transmission electron microscopes suggest that material's properties such as electrical conductivity, connectivity, and work function can be probed with atomic scale resolution using a technique known as secondary electron e-beam-induced current (SEEBIC). Here, we apply the SEEBIC imaging technique to a stacked 2D heterostructure device to reveal the spatially resolved electron orbital ionization cross section of an encapsulated WSe2 layer. We find that the double Se lattice site shows higher emission than the W site, which is at odds with first-principles modelling of ionization of an isolated WSe2 cluster. These results illustrate that atomic level SEEBIC contrast within a single material is possible and that an enhanced understanding of atomic scale SE emission is required to account for the observed contrast. In turn, this suggests that subtle information about interlayer bonding and the effect on electron orbitals can be directly revealed with this technique.

cond-mat.mtrl-sci↗

arXiv2022

Enabling Autonomous Electron Microscopy for Networked Computation and Steering

Advanced electron microscopy workflows require an ecosystem of microscope instruments and computing systems possibly located at different sites to conduct remotely steered and automated experiments. Current workflow executions involve manual operations for steering and measurement tasks, which are typically performed from control workstations co-located with microscopes; consequently, their operational tempo and effectiveness are limited. We propose an approach based on separate data and control channels for such an ecosystem of Scanning Transmission Electron Microscopes (STEM) and computing systems, for which no general solutions presently exist, unlike the neutron and light source instruments. We demonstrate automated measurement transfers and remote steering of Nion STEM physical instruments over site networks. We propose a Virtual Infrastructure Twin (VIT) of this ecosystem, which is used to develop and test our steering software modules without requiring access to the physical instrument infrastructure. Additionally, we develop a VIT for a multiple laboratory scenario, which illustrates the applicability of this approach to ecosystems connected over wide-area networks, for the development and testing of software modules and their later field deployment.

cs.DC↗

arXiv2022

A roadmap for edge computing enabled automated multidimensional transmission electron microscopy

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since such datasets' analysis, collection, long-term storage, and networking remain challenging. Some common issues are the large and unwieldy size of the said datasets, often running into several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope fully. However, the existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is exceptionally challenging. One solution is integrating microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to HPC systems. In this perspective, we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

cond-mat.mtrl-sci↗

arXiv2022

Mapping Conductance and Switching Behavior of Graphene Devices In Situ

Graphene has been proposed for use in various nanodevice designs, many of which harness emergent quantum properties for device functionality. However, visualization, measurement, and manipulation become non-trivial at nanometer and atomic scales, representing a significant challenge for device fabrication, characterization, and optimization at length scales where quantum effects emerge. Here, we present proof of principle results at the crossroads between 2D nanoelectronic devices, e-beam-induced modulation, and imaging with secondary electron e-beam induced currents (SEEBIC). We introduce a device platform compatible with scanning transmission electron microscopy investigations. We then show how the SEEBIC imaging technique can be used to visualize conductance and connectivity in single layer graphene nanodevices, even while supported on a thicker substrate (conditions under which conventional imaging fails). Finally, we show that the SEEBIC imaging technique can detect subtle differences in charge transport through time in non-ohmic graphene nanoconstrictions indicating the potential to reveal dynamic electronic processes.

cond-mat.mes-hall↗

arXiv2022

Electron-beam Introduction of Heteroatomic Pt-Si Structures in Graphene

Electron-beam (e-beam) manipulation of single dopant atoms in an aberration-corrected scanning transmission electron microscope is emerging as a method for directed atomic motion and atom-by-atom assembly. Until now, the dopant species have been limited to atoms closely matched to carbon in terms of ionic radius and capable of strong covalent bonding with carbon atoms in the graphene lattice. In situ dopant insertion into a graphene lattice has thus far been demonstrated only for Si, which is ubiquitously present as a contaminant in this material. Here, we achieve in situ manipulation of Pt atoms and their insertion into the graphene host matrix using the e-beam deposited Pt on graphene as a host system. We further demonstrate a mechanism for stabilization of the Pt atom, enabled through the formation of Si-stabilized Pt heteroatomic clusters attached to the graphene surface. This study provides evidence toward the universality of the e-beam assembly approach, opening a pathway for exploring cluster chemistry through direct assembly.

cond-mat.mtrl-sci↗

arXiv2022

Contrast mechanisms in secondary electron e-beam induced current (SEEBIC) imaging

Over the last few years, a new mode for imaging in the scanning transmission electron microscope (STEM) has gained attention as it permits the direct visualization of sample conductivity and electrical connectivity. When the electron beam (e-beam) is focused on the sample in the STEM, secondary electrons (SEs) are generated. If the sample is conductive and electrically connected to an amplifier, the SE current can be measured as a function of the e-beam position. This scenario is similar to the better-known scanning electron microscopy (SEM)-based technique, electron beam induced current (EBIC) imaging except the signal in STEM is generated by the emission of SEs, hence the name SEEBIC, and in this case the current flows in the opposite direction. Here, we provide a brief review of recent work in this area, examine the various contrast generation mechanisms associated with SEEBIC, and illustrate its use for the characterization of graphene nanoribbon devices.

cond-mat.mtrl-sci↗

arXiv2022

Discovering Invariant Spatial Features in Electron Energy Loss Spectroscopy Images on the Mesoscopic and Atomic Levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope (STEM) has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional datasets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally-invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader here.

cond-mat.mtrl-sci↗

arXiv2021

Spatial symmetry constraint of charge-ordered kagome superconductor CsV$_3$Sb$_5$

Elucidating the symmetry of intertwined orders in exotic superconductors is at the quantum frontier. Recent surface sensitive studies of the topological kagome superconductor CsV$_3$Sb$_5$ discovered a cascade 4a$_0$ superlattice below the charge density wave (CDW) ordering temperature, which can be related to the pair density modulations in the superconducting state. If the 4a$_0$ phase is a bulk and intrinsic property of the kagome lattice, this would form a striking analogy to the stripe order and pair density wave discovered in the cuprate high-temperature superconductors, and the cascade ordering found in twisted bilayer graphene. High-resolution X-ray diffraction has recently been established as an ultra-sensitive probe for bulk translational symmetry-breaking orders, even for short-range orders at the diffusive limit. Here, combining high-resolution X-ray diffraction, scanning tunneling microscopy and scanning transmission electron microscopy, we demonstrate that the 4a$_0$ superstructure emerges uniquely on the surface and hence exclude the 4a$_0$ phase as the origin of any bulk transport or spectroscopic anomaly. Crucially, we show that our detected 2$\times$2$\times$2 CDW order breaks the bulk rotational symmetry to C2, which can be the driver for the bulk nematic orders and nematic surface superlattices including the 4a$_0$ phase. Our high-resolution data impose decisive spatial symmetry constraints on emergent electronic orders in the kagome superconductor CsV$_3$Sb$_5$.

cond-mat.mtrl-sci↗

arXiv2021

Correlated Oxide Dirac Semimetal in the Extreme Quantum Limit

Quantum materials (QMs) with strong correlation and non-trivial topology are indispensable to next-generation information and computing technologies. Exploitation of topological band structure is an ideal starting point to realize correlated topological QMs. Herein, we report that strain-induced symmetry modification in correlated oxide SrNbO3 thin films creates an emerging topological band structure. Dirac electrons in strained SrNbO3 films reveal ultra-high mobility (100,000 cm2/Vs), exceptionally small effective mass (0.04me), and non-zero Berry phase. More importantly, strained SrNbO3 films reach the extreme quantum limit, exhibiting a sign of fractional occupation of Landau levels and giant mass enhancement. Our results suggest that symmetry-modified SrNbO3 is a rare example of a correlated topological QM, in which strong correlation of Dirac electrons leads to the realization of fractional occupation of Landau levels.

cond-mat.str-el↗

arXiv2021

Atomic structure of initial nucleation layer in hexagonal perovskite BaRuO$_3$ thin films

Hexagonal perovskites are an attractive group of materials due to their various polymorph phases and rich structure-property relationships. BaRuO3 (BRO) is a prototypical hexagonal perovskite, in which the electromagnetic properties are significantly modified depending on its atomic structure. Whereas thin-film epitaxy would vastly expand the application of hexagonal perovskites by epitaxially stabilizing various metastable polymorphs, the atomic structure of epitaxial hexagonal perovskites, especially at the initial growth stage, has rarely been investigated. In this study, we show that an intriguing nucleation behavior takes place during the initial stabilization of a hexagonal perovskite 9R BaRuO3 (BRO) thin film on a (111) SrTiO3 (STO) substrate. We use high-resolution high-angle annular dark field scanning transmission electron microscopy in combination with geometrical phase analysis to understand the local strain relaxation behavior. We find that nano-scale strained layers, composed of different RuO6 octahedral stacking, are initially formed at the interface, followed by a relaxed single crystal9R BRO thin film. Within the interface layer, hexagonal BROs are nucleated on the STO (111) substrate by both corner- and face-sharing. More interestingly, we find that the boundaries between the differently-stacked nucleation layers, i.e. heterostructural boundaries facilitates strain relaxation, in addition to the formation of conventional misfit dislocations evolving from homostructural boundaries. Our observations reveal an important underlying mechanism to understand the thin-film epitaxy and strain accommodation in hexagonal perovskites.

cond-mat.mtrl-sci↗

arXiv2021

Twin-domain formation in epitaxial triangular lattice delafossites

Twin domains are often found as structural defects in symmetry mismatched epitaxial thin films. The delafossite ABO2, which has a rhombohedral structure, is a good example that often forms twin domains. Although bulk metallic delafossites are known to be the most conducting oxides, the high conductivity is yet to be realized in thin film forms. Suppressed conductivity found in thin films is mainly caused by the formation of twin domains, and their boundaries can be a source of scattering centers for charge carriers. To overcome this challenge, the underlying mechanism for their formation must be understood, so that such defects can be controlled and eliminated. Here, we report the origin of structural twins formed in a CuCrO2 delafossite thin film on a substrate with hexagonal or triangular symmetries. A robust heteroepitaxial relationship is found for the delafossite film with the substrate, and the surface termination turns out to be critical to determine and control the domain structure of epitaxial delafossites. Based on such discoveries, we also demonstrate a twin-free epitaxial thin films grown on high-miscut substrates. This finding provides an important synthesis strategy for growing single domain delafossite thin films and can be applied to other delafossites for epitaxial synthesis of high-quality thin films.

cond-mat.mtrl-sci↗

arXiv2021

Sculpting the plasmonic responses of nanoparticles by directed electron beam irradiation

Spatial confinement of matter in functional nanostructures has propelled these systems to the forefront of nanoscience, both as a playground for exotic physics and quantum phenomena and in multiple applications including plasmonics, optoelectronics, and sensing. In parallel, the emergence of monochromated electron energy loss spectroscopy (EELS) has enabled exploration of local nanoplasmonic functionalities within single nanoparticles and the collective response of nanoparticle assemblies, providing deep insight into the associated mechanisms. However, modern synthesis processes for plasmonic nanostructures are often limited in the types of accessible geometry and materials, and even then, limited to spatial precisions on the order of tens of nm, precluding the direct exploration of critical aspects of the structure-property relationships. Here, we use the atomic-sized probe of the scanning transmission electron microscope (STEM) to perform precise sculpting and design of nanoparticle configurations. Furthermore, using low-loss (EELS), we provide dynamic analyses of evolution of the plasmonic response during the sculpting process. We show that within self-assembled systems of nanoparticles, individual nanoparticles can be selectively removed, reshaped, or arbitrarily patterned with nanometer-level resolution, effectively modifying the plasmonic response in both space and energy domains. This process significantly increases the scope for design possibilities and presents opportunities for arbitrary structure development, which are ultimately key for nanophotonic design. Nanosculpting introduces yet another capability to the electron microscope.

cond-mat.mtrl-sci↗

arXiv2021

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment (AE) in imaging. Here, we aim to analyze the major pathways towards AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment, and consider the latencies, biases, and knowledge priors of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning.

cs.LG↗

arXiv2020

Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

The 4D scanning transmission electron microscopy (STEM) method has enabled mapping of the structure and functionality of solids on the atomic scale, yielding information-rich data sets containing information on the interatomic electric and magnetic fields, structural and electronic order parameters, and other symmetry breaking distortions. A critical bottleneck on the pathway toward harnessing 4D-STEM for materials exploration is the dearth of analytical tools that can reduce complex 4D-STEM data sets to physically relevant descriptors. Classical machine learning (ML) methods such as principal component analysis and other linear unmixing techniques are limited by the presence of multiple point-group symmetric variants, where diffractograms from each rotationally equivalent position will form its own component. This limitation even holds for more complex ML methods, such as convolutional neural networks. Here, we propose and implement an approach for the systematic exploration of symmetry breaking phenomena from 4D-STEM data sets using rotationally invariant variational autoencoders (rrVAE), which is designed to disentangle the general rotation of the object from other latent representations. The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures that illustrate the effect of site symmetry breaking. Finally, the rrVAE analysis of 4D-STEM data of vacancies in graphene is illustrated and compared to the classical center-of-mass (COM) analysis. This approach is universal for probing of symmetry breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods exploring the 2D diffraction space of the system, including X-ray ptychography, electron backscatter diffraction (EBSD), and more complex methods.

physics.comp-ph↗

arXiv2020

Gaussian process analysis of Electron Energy Loss Spectroscopy (EELS) data: parallel reconstruction and kernel control

Advances in hyperspectral imaging modes including electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM) bring forth the challenges of exploratory and subsequently physics-based analysis of multidimensional data sets. The (by now common) multivariate unsupervised linear unmixing methods and their nonlinear analogs generally explore similarities in the energy dimension but ignore correlations in the spatial domain. At the same time, Gaussian process (GP) methods that explicitly incorporate spatial correlations in the form of kernel functions tend to be extremely computationally intensive, while the use of inducing point-based sparse methods often leads to reconstruction artefacts. Here, we suggest and implement a parallel GP method operating on the full spatial domain and reduced representations in the energy domain. In this parallel GP, the information between the components is shared via a common spatial kernel structure while allowing for variability in the relative noise magnitude or image morphology. We explore the role of common spatial structures and kernel constraints on the quality of the reconstruction and suggest an approach for estimating these factors from the experimental data. Application of this method to an example EELS dataset demonstrates that spatial information contained in higher-order components can be reconstructed and spatially localized. This approach can be further applied to other hyperspectral and multimodal imaging modes. The notebooks developed in this manuscript are freely available as part of a GPim package (https://github.com/ziatdinovmax/GPim).

physics.comp-ph↗