arXiv subjects

Stephen Jesse

Publications and source records attributed to Stephen Jesse.

At least 73 records · Page 4Linked to original sources

arXiv2018

Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2

Understanding elementary mechanisms behind solid-state phase transformations and reactions is the key to optimizing desired functional properties of many technologically relevant materials. Recent advances in scanning transmission electron microscopy (STEM) allow the real-time visualization of solid-state transformations in materials, including those induced by an electron beam and temperature, with atomic resolution. However, despite the ever-expanding capabilities for high-resolution data acquisition, the inferred information about kinetics and thermodynamics of the process and single defect dynamics and interactions is minima, due to the inherent limitations of manual ex-situ analysis of the collected volumes of data. To circumvent this problem, we developed a deep learning framework for dynamic STEM imaging that is trained to find the structures (defects) that break a crystal lattice periodicity and apply it for mapping solid state reactions and transformations in layered WS2 doped with Mo. This framework allows extracting thousands of lattice defects from raw STEM data (single images and movies) in a matter of seconds, which are then classified into different categories using unsupervised clustering methods. We further expanded our framework to extract parameters of diffusion for the sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy, providing insight into point defect dynamics and reactions. This approach is universal and its application to beam induced reactions allows mapping chemical transformation pathways in solids at the atomic level.

cond-mat.mtrl-sci↗

arXiv2018

Assembling Di- and Multiatomic Si Clusters in Graphene via Electron Beam Manipulation

We demonstrate assembly of di-, tri- and tetrameric Si clusters on the graphene surface using sub-atomically focused electron beam of a scanning transmission electron microscope. Here, an electron beam is used to introduce Si substitutional defects and defect clusters in graphene with spatial control of a few nanometers, and enable controlled motion of Si atoms. The Si substitutional defects are then further manipulated to form dimers, trimers and more complex structures. The dynamics of a beam induced atomic scale chemical process is captured in a time-series of images at atomic resolution. These studies suggest that control of the e-beam induced local processes offers the next step toward atom-by-atom nanofabrication and provides an enabling tool for study of atomic scale chemistry in 2D materials.

cond-mat.mtrl-sci↗

arXiv2018

Single atom force measurements: mapping potential energy landscapes via electron beam induced single atom dynamics

In the last decade, the atomically focused beam of a scanning transmission electron microscope (STEM) was shown to induce a broad set of transformations of material structure, open pathways for probing atomic-scale reactions and atom-by-atom matter assembly. However, the mechanisms of beam-induced transformations remain largely unknown, due to an extreme mismatch between the energy and time scales of electron passage through solids and atomic and molecular motion. Here, we demonstrate that a single dopant Si atom in the graphene lattice can be used as an atomic scale force sensor, providing information on the random force exerted by the beam on chemically-relevant time scales. Using stochastic reconstruction of molecular dynamic simulations, we recover the potential energy landscape of the atom and use it to determine the beam-induced effects in the thermal (i.e. white noise) approximation. We further demonstrate that the moving atom under beam excitation can be used to map potential energy along step edges, providing information about atomic-scale potentials in solids. These studies open the pathway for quantitative studies of beam-induced atomic dynamics, elementary mechanisms of solid-state transformations, and predictive atom-by-atom fabrication.

cond-mat.mtrl-sci↗

arXiv2018

Mapping mesoscopic phase evolution during e-beam induced transformations via deep learning of atomically resolved images

Understanding transformations under electron beam irradiation requires mapping the structural phases and their evolution in real time. To date, this has mostly been a manual endeavor comprising of difficult frame-by-frame analysis that is simultaneously tedious and prone to error. Here, we turn towards the use of deep convolutional neural networks (DCNN) to automatically determine the Bravais lattice symmetry present in atomically-resolved images. A DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier transform of the input image. Monte-Carlo dropout is used for determining the prediction probability, and results are shown for both simulated and real atomically-resolved images from scanning tunneling microscopy and scanning transmission electron microscopy. A reduced representation of the final layer output allows to visualize the separation of classes in the DCNN and agrees with physical intuition. We then apply the trained network to electron beam-induced transformations in WS2, which allows tracking and determination of growth rate of voids. These results are novel in two ways: (1) It shows that DCNNs can be trained to recognize diffraction patterns, which is markedly different from the typical "real image" cases, and (2) it provides a method with in-built uncertainty quantification, allowing the real-time analysis of phases present in atomically resolved images.

cond-mat.mtrl-sci↗

arXiv2018

Mapping fast evolution of transient surface photovoltage dynamics using G-Mode Kelvin probe force microscopy

Optoelectronic phenomena in materials such as organic/inorganic hybrid perovskites depend on a complex interplay between light induced carrier generation and fast (electronic) and slower (ionic) processes, all of which are known to be strongly affected by structural inhomogeneities such as interfaces and grain boundaries. Here, we develop a time resolved Kelvin probe force microscopy (KPFM) approach, based on the G-Mode SPM platform, allowing quantification of surface photovoltage (SPV) with microsecond temporal and nanoscale spatial resolution. We demonstrate the approach on methylammonium lead bromide (MAPbBr3) thin films and further highlight the usefulness of unsupervised clustering methods to quickly discern spatial variability in the information rich SPV dataset. Using this technique, we observe concurrent spatial and ultra-fast temporal variations in the SPV generated across the thin film, indicating that structure is likely responsible for the heterogenous behavior.

physics.app-ph↗

arXiv2018

Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This progress has been accompanied by an exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extract information from atomically resolved images including location of the atomic species and type of defects. We develop a 'weakly-supervised' approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular 'rotor'. This deep learning based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data.

cond-mat.mtrl-sci↗

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 Probing of Polarization Charges at the Nanoscale

Ferroelectric materials possess spontaneous polarization that can be used for multiple applications. Owing to a long term development for reducing the sizes of devices, the preparation of ferroelectric materials and devices are entering nanometer-scale regime. Accordingly, to evaluate the ferroelectricity, there is a need to investigate the polarization charge at the nanoscale. Nonetheless, it has been generally accepted that the detection of polarization charges using a conventional conductive atomic force microscopy (CAFM) without a top electrode is not feasible because the nanometer-scale radius of an atomic force microscopy (AFM) tip yields a very low signal-to-noise ratio. However, the detection is unrelated to the radius of an AFM tip, and in fact, a matter of the switched area. In this work, we demonstrate the direct probing of the polarization charge at the nanoscale using the Positive-Up-Negative-Down method based on the conventional CAFM approach without additional corrections or circuits to reduce the parasitic capacitance. We successfully probed the polarization charge densities of 73.7 and 119.0 uC/cm2 in ferroelectric nanocapacitors and thin films, respectively. The obtained results show the feasibility of the evaluation of the polarization charge at the nanoscale and provide a new guideline for evaluating the ferroelectricity at the nanoscale.

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↗

arXiv2017

E-beam manipulation of Si atoms on graphene edges with aberration-corrected STEM

The burgeoning field of atomic level material control holds great promise for future breakthroughs in quantum and memristive device manufacture and fundamental studies of atomic-scale chemistry. Realization of atom-by atom control of matter represents a complex and ongoing challenge. Here, we explore the feasibility of controllable motion of dopant Si atoms at the edges of graphene via the sub-atomically focused electron beam in a scanning transmission electron microscope (STEM). We demonstrate that the graphene edges can be cleaned of Si atoms and then subsequently replenished from nearby source material. It is also shown how Si edge atoms may be pushed from the edge of a small hole into the bulk of the graphene lattice and from the bulk of the lattice back to the edge. This is accomplished through sputtering of the edge of the graphene lattice to bury or uncover Si dopant atoms. These experiments form an initial step toward general atomic scale material control.

cond-mat.mtrl-sci↗

arXiv2017

Mitigating E-beam-induced Hydrocarbon Deposition on Graphene for Atomic-Scale Scanning Transmission Electron Microscopy Studies

CVD grown graphene used in (scanning) transmission electron microscopy ((S)TEM) studies must undergo a careful transfer of the one-atom-thick membrane from the growth surface (typically a Cu foil) to the TEM grid. During this transfer process, the graphene invariably becomes contaminated with foreign material. This contamination proves to be very problematic in the (S)TEM because often >95% of the graphene is obscured and imaging of the pristine areas results in e-beam-induced hydrocarbon deposition which further acts to obscure the desired imaging area. In this article, we examine two cleaning techniques for CVD grown graphene that mitigate both aspects of the contamination problem: visible contamination covering the graphene, and invisible contamination that deposits onto the graphene under e-beam irradiation. The visible contamination may be removed quickly by a rapid thermal annealing to 1200 C in situ and the invisible e-beam-deposited contamination may be removed through an Ar/O2 annealing procedure prior to imaging in the (S)TEM.

cond-mat.mtrl-sci↗

arXiv2017

Two-Level Structural Sparsity Regularization for Identifying Lattices and Defects in Noisy Images

This paper presents a regularized regression model with a two-level structural sparsity penalty applied to locate individual atoms in a noisy scanning transmission electron microscopy image (STEM). In crystals, the locations of atoms is symmetric, condensed into a few lattice groups. Therefore, by identifying the underlying lattice in a given image, individual atoms can be accurately located. We propose to formulate the identification of the lattice groups as a sparse group selection problem. Furthermore, real atomic scale images contain defects and vacancies, so atomic identification based solely on a lattice group may result in false positives and false negatives. To minimize error, model includes an individual sparsity regularization in addition to the group sparsity for a within-group selection, which results in a regression model with a two-level sparsity regularization. We propose a modification of the group orthogonal matching pursuit (gOMP) algorithm with a thresholding step to solve the atom finding problem. The convergence and statistical analyses of the proposed algorithm are presented. The proposed algorithm is also evaluated through numerical experiments with simulated images. The applicability of the algorithm on determination of atom structures and identification of imaging distortions and atomic defects was demonstrated using three real STEM images. We believe this is an important step toward automatic phase identification and assignment with the advent of genomic databases for materials.

stat.AP↗

arXiv2017

Single atom manipulation and control in a scanning transmission electron microscope

We demonstrate that the sub-atomically focused beam of a scanning transmission electron microscope (STEM) can be used to controllably manipulate individual dopant atoms in a 2D graphene lattice. We demonstrate the manipulation of adsorbed source materials and the graphene lattice with the electron beam such that individual vacancy defects can be controllably passivated by Si substitutional atoms. We further demonstrate that these Si defects may be directed through the lattice via e-beam control or modified (as yet, uncontrollably) to form new defects which can incorporate new atoms into the graphene lattice. These studies demonstrate the potential of STEM for atom-by-atom nanofabrication and fundamental studies of chemical reactions in 2D materials on the atomic level.

cond-mat.mtrl-sci↗

arXiv2017

Ferroelectric or non-ferroelectric: why so many materials exhibit ferroelectricity on the nanoscale

Ferroelectric materials have remained one of the foci of condensed matter physics and materials science for over 50 years. In the last 20 years, the development of voltage-modulated scanning probe microscopy techniques, exemplified by Piezoresponse force microscopy (PFM) and associated time and voltage spectroscopies, opened a pathway to explore these materials on a single-digit nanometer level. Consequently, domain structures, walls and polarization dynamics can now be imaged in real space. More generally, PFM has allowed studying electromechanical coupling in a broad variety of materials ranging from ionics to biological systems. It can also be anticipated that the recent Nobel prize in molecular electromechanical machines will result in rapid growth in interest in PFM as a method to probe their behavior on single device and device assembly levels. However, the broad introduction of PFM also resulted in a growing number of reports on nearly ubiquitous presence of ferroelectric-like phenomena including remnant polar states and electromechanical hysteresis loops in materials which are non-ferroelectric in the bulk, or in cases where size effects are expected to suppress ferroelectricity. While in certain cases plausible physical mechanisms can be suggested, there is remarkable similarity in observed behaviors, irrespective of the materials system. In this review, we summarize the basic principles of PFM, briefly discuss the features of ferroelectric surfaces salient to PFM imaging and spectroscopy, and summarize existing reports on ferroelectric like responses in non-classical ferroelectric materials. We further discuss possible mechanisms behind observed behaviors, and possible experimental strategies for their identification.

cond-mat.mtrl-sci↗

arXiv2016

Quantification of Probe-Sample Electrostatic Forces with Dynamic Atomic Force Microscopy

Atomic Force Microscopy (AFM) methods utilizing resonant mechanical vibrations of cantilevers in contact with a sample surface have shown sensitivities as high as few picometers for detecting surface displacements. Such a high sensitivity is harnessed in several AFM imaging modes. Here, we demonstrate a cantilever-resonance-based method to quantify electrostatic forces on a probe arising in the presence of a surface potential or when a bias voltage is applied to the AFM probe. We find that the electrostatic forces acting on the probe tip apex can strongly dominate cantilever response in electromechanical measurements and produce signals equivalent to few pm of surface displacement. In combination with modeling, the measurements of the force were used to determine the strength of the electrical field at the probe tip apex in contact with a sample. We find an evidence that the electric field strength in the junctions is limited by about 0.5 V/nm. This field can be sufficiently strong to significantly influence material states and kinetic processes through charge injection, Maxwell stress, shifts of phase equilibria, and reduction of energy barriers for activated processes. Besides, the results provide a baseline for accounting for the effects of local electrostatic forces in electromechanical AFM measurements as well as offer additional means to probe ionic mobility and field-induced phenomena in solids.

cond-mat.mes-hall↗

arXiv2016

Data Mining Graphene: Correlative Analysis of Structure and Electronic Degrees of Freedom in Graphenic Monolayers with Defects

The link between changes in the material crystal structure and its mechanical, electronic, magnetic, and optical functionalities - known as the structure-property relationship - is the cornerstone of the contemporary materials science research. The recent advances in scanning transmission electron and scanning probe microscopies (STEM and SPM) have opened an unprecedented path towards examining the materials structure-property relationships on the single-impurity and atomic-configuration levels. Lacking, however, are the statistics-based approaches for cross-correlation of structure and property variables obtained in different information channels of the STEM and SPM experiments. Here we have designed an approach based on a combination of sliding window fast Fourier transform, Pearson correlation matrix, and linear and kernel canonical correlation, to study a relationship between lattice distortions and electron scattering from SPM data on graphene with defects. Our analysis revealed that the strength of coupling to strain is altered between different scattering channels, which can explain coexistence of several quasiparticle interference patterns in nanoscale regions of interest. In addition, the application of kernel functions allowed us to extract a non-linear component of the relationship between the lattice strain and scattering intensity in graphene. The outlined approach can be further utilized towards analyzing correlations in various multi-modal imaging techniques where the information of interest is spatially distributed and generally has a complex multidimensional nature.

cond-mat.mtrl-sci↗

arXiv2016

Quantification of surface displacements and electromechanical phenomena via dynamic atomic force microscopy

Detection of dynamic surface displacements associated with local changes in material strain provides access to a number of phenomena and material properties. Contact resonance-enhanced methods of Atomic Force Microscopy (AFM) have been shown capable of detecting ~1-3 pm-level surface displacements, an approach used in techniques such as piezoresponse force microscopy, atomic force acoustic microscopy, and ultrasonic force microscopy. Here, based on an analytical model of AFM cantilever vibrations, we demonstrate a guideline to quantify surface displacements with a high accuracy by taking into account the cantilever shape at the first resonant contact mode depending on the tip-sample contact stiffness. The approach has been experimentally verified and further developed for the piezoresponse force microscopy (PFM) using well-defined ferroelectric materials. These results open up a way to accurate and precise measurements of the surface displacement as well as piezoelectric constants at the pm-scale with nanometer spatial resolution and will allow avoiding erroneous data interpretations and measurement artefacts. This analysis is directly applicable to all cantilever-resonance-based SPM techniques.

cond-mat.mtrl-sci↗

arXiv2012

Ionically-mediated electromechanical hysteresis in transition metal oxides

Electromechanical activity, remanent polarization states, and hysteresis loops in paraelectric TiO2 and SrTiO3 are observed. The coupling between the ionic dynamics and incipient ferroelectricity in these materials is analyzed using extended Ginsburg Landau Devonshire (GLD) theory. The possible origins of electromechanical coupling including ionic dynamics, surface-charge induced electrostriction, and ionically-induced ferroelectricity are identified. For the latter, the ionic contribution can change the sign of first order GLD expansion coefficient, rendering material effectively ferroelectric. These studies provide possible explanation for ferroelectric-like behavior in centrosymmetric transition metal oxides.

cond-mat.mtrl-sci↗