arXiv subjects

Andrew R. Lupini

Publications and source records attributed to Andrew R. Lupini.

44 records · Page 3Linked to original sources

arXiv2020

Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening

Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D scanning transmission electron microscopy (STEM) datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO3 and SrTiO3, using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We validate the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and achieve 85% accuracy in determination of buried step positions within the interface. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.

cond-mat.mes-hall↗

arXiv2019

Structure retrieval from 4D-STEM: statistical analysis of potential pitfalls in high-dimensional data

Four-dimensional scanning transmission electron microscopy (4D-STEM) is one of the most rapidly growing modes of electron microscopy imaging. The advent of fast pixelated cameras and the associated data infrastructure have greatly accelerated this process. Yet conversion of the 4D datasets into physically meaningful structure images in real-space remains an open issue. In this work, we demonstrate that, it is possible to systematically create filters that will affect the apparent resolution or even qualitative features of the real-space structure image, reconstructing artificially generated patterns. As initial efforts, we explore statistical model selection algorithms, aiming for robustness and reliability of estimated filters. This statistical model selection analysis demonstrates the need for regularization and cross-validation of inversion methods to robustly recover structure from high-dimensional diffraction datasets.

stat.AP↗

arXiv2019

USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data

Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variety and complexity. Harnessing these data for scientific research requires an evolution of the associated computing and data infrastructure, bridging scientific instrumentation with super- and cloud- computing. Here, we describe Universal Spectroscopy and Imaging Data (USID), a data model capable of representing data from most common instruments, modalities, dimensionalities, and sizes. We pair this schema with the hierarchical data file format (HDF5) to maximize compatibility, exchangeability, traceability, and reproducibility. We discuss a family of community-driven, open-source, and free python software packages for storing, processing and visualizing data. The first is pyUSID which provides the tools to read and write USID HDF5 files in addition to a scalable framework for parallelizing data analysis. The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID. The instrument-agnostic nature of USID facilitates the development of analysis code independent of instrumentation and task in Pycroscopy which in turn can bring scientific communities together and break down barriers in the age of open-science. The interested reader is encouraged to be a part of this ongoing community-driven effort to collectively accelerate materials research and discovery through the realms of big data.

physics.data-an↗

arXiv2019

Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold leaning approaches for straightforward visualization and exploration analysis of the 4D-STEM datasets, distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene, with single dopant atoms, as recorded on a pixelated detector. These extracted patterns relate to both individual atom sites and sublattice structures, effectively discriminating single dopant anomalies via multi-mode views. We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric, topological spin and van der Waals heterostructures.

eess.IV↗

arXiv2018

Deep analytics of atomically-resolved images: manifest and latent features

Recent advances in scanning transmission electron and scanning tunneling microscopies allow researchers to measure materials structural and electronic properties, such as atomic displacements and charge density modulations, at an Angstrom scale in real space. At the same time, the ability to quickly acquire large, high-resolution datasets has created a challenge for rapid physics-based analysis of images that typically contain several hundreds to several thousand atomic units. Here we demonstrate a universal deep-learning based framework for locating and characterizing atomic species in the lattice, which can be applied to different types of atomically resolved measurements on different materials. Specifically, by inspecting and categorizing features in the output layer of a convolutional neural network, we are able to detect structural and electronic 'anomalies' associated with the presence of point defects in a tungsten disulfide monolayer, non-uniformity of the charge density distribution around specific lattice sites on the surface of strongly correlated oxides, and transition between different structural states of buckybowl molecules. We further extended our method towards tracking, from one image frame to another, minute distortions in the geometric shape of individual Si dumbbells in a 3-dimensional Si sample, which are associated with a motion of lattice defects and impurities. Due the applicability of our framework to both scanning tunneling microscopy and scanning transmission electron microscopy measurements, it can provide a fast and straightforward way towards creating a unified database of defect-property relationships from experimental data for each material.

cond-mat.mtrl-sci↗

arXiv2017

Direct atomic fabrication and dopant positioning in Si using electron beams with active real time image-based feedback

Semiconductor fabrication is a mainstay of modern civilization, enabling the myriad applications and technologies that underpin everyday life. However, while sub-10 nanometer devices are already entering the mainstream, the end of the Moore's Law roadmap still lacks tools capable of bulk semiconductor fabrication on sub-nanometer and atomic levels, with probe-based manipulation being explored as the only known pathway. Here we demonstrate that the atomic-sized focused beam of a scanning transmission electron microscope can be used to manipulate semiconductors such as Si on the atomic level, inducing growth of crystalline Si from the amorphous phase, reentrant amorphization, milling, and dopant-front motion. These phenomena are visualized in real time with atomic resolution. We further implement active feedback control based on real-time image analytics to control the e-beam motion, enabling shape control and providing a pathway for atom-by-atom correction of fabricated structures in the near future. These observations open a new epoch for atom-by-atom manufacturing in bulk, the long-held dream of nanotechnology.

cond-mat.mtrl-sci↗

arXiv2014

Persistent photoconductivity in 2-dimensional electron gases at different oxide interfaces

We report on the transport characterization in dark and under light irradiation of three different interfaces: LaAlO3/SrTiO3, LaGaO3/SrTiO3, and the novel NdGaO3/SrTiO3 heterostructure. All of them share a perovskite structure, an insulating nature of the single building blocks, a polar/non- polar character and a critical thickness of four unit cells for the onset of conductivity. The interface structure and charge confinement in NdGaO3/SrTiO3 are probed by atomic-scale- resolved electron energy loss spectroscopy showing that, similarly to LaAlO3/SrTiO3, extra electronic charge confined in a sheet of about 1.5 nm in thickness is present at the NdGaO3/SrTiO3 interface. Electric transport measurements performed in dark and under radiation show remarkable similarities and provide evidence that the persistent perturbation induced by light is an intrinsic peculiar property of the three investigated oxide-based polar/non-polar interfaces. Our work sets a framework for understanding the previous contrasting results found in literature about photoconductivity in LaAlO3/SrTiO3 and highlights the connection between the origin of persistent photoconductivity and the origin of conductivity itself. An improved understanding of the photo- induced metastable electron-hole pairs might allow to shed a direct light on the complex physics of this system and on the recently proposed perspectives of oxide interfaces for solar energy conversion.

cond-mat.str-el↗

arXiv2012

Reversible and Persistent Photoconductivity at the NdGaO3/SrTiO3 Conducting Interface

The interface between the band gap insulators LaAlO3 and SrTiO3 is known to host a highly mobile two-dimensional electron gas. Here we report on the fabrication and characterization of the NdGaO3/SrTiO3 interface, that shares with LaAlO3/SrTiO3 an all-perovskite structure, the insulating nature of the single building block and the polar-non polar character. Our work demonstrates that in NdGaO3/SrTiO3 a metallic layer of mobile electrons is formed, with properties comparable to LaAlO3/SrTiO3. The localization of the injected electrons at the Ti sites, within a few unit cells from the interface, was proved by Atomic-scale-resolved EELS analyses. The electric transport and photoconduction of samples were also investigated. We found that irradiation by photons below the SrTiO3 gap does not increase the carrier density, but slightly enhances low temperature mobility. A giant persistent photoconductivity effect was instead observed, even under irradiation by low energy photons, in highly resistive samples fabricated at non-optimal conditions. We discuss the results in the light of different mechanisms proposed for the two-dimensional electron gas formation. Both the ordinary and the persistent photoconductivity in these systems are addressed and analyzed.

cond-mat.mtrl-sci↗