arXiv subjects

Andreas Rosenauer

Publications and source records attributed to Andreas Rosenauer.

5 recordsLinked to original sources

arXiv2026

Modelling the mean inner potential of alloyed and strained materials

In this publication, we study the influence of strain and alloying on the mean inner potential (MIP) using density functional theory (DFT) within an augmented plane waves plus local orbitals basis set. Two major effects have been identified allowing to model the influence of strain and alloying on the mean inner potential with a reasonable accuracy. First, alloying for constant volume results in a linear relationship between the MIP and the concentration. Second, the MIP scales with changes in volume as we already pointed out in an earlier publication (M. Schowalter, D. Lamoen, A. Rosenauer, P. Kruse, and D. Gerthsen, Appl. Phys. Lett. 85, 4938-4940 (2004)). Specifically, a linear relationship between MIP and concentration x was found for AlGaAs (nearly no change in lattice parameter), whereas InGaP and GeSi (volume changes with concentration x) exhibits a clear bowing. The bowing can be modeled by taking the rescaling of the MIP with the varying volume additionally into account. The rescaling could be also used to model the dependence of the MIP on strained binary cells and the density dependence of e.g. amorphous materials.

cond-mat.mtrl-sci↗

arXiv2023

Growth, catalysis and faceting of $α$-Ga$_2$O$_3$ and $α$-(In$_x$Ga$_{1-x}$)$_2$O$_3$ on $m$-plane $α$-Al$_2$O$_3$ by molecular beam epitaxy

The growth of $α$-Ga$_2$O$_3$ and $α$-(In$_x$Ga$_{1-x}$)$_2$O$_3$ on $m$-plane $α$-Al$_2$O$_3$(10$\bar{1}$0) by molecular beam epitaxy (MBE) and metal-oxide-catalyzed epitaxy (MOCATAXY) is investigated. By systematically exploring the parameter space accessed by MBE and MOCATAXY, phase-pure $α$-Ga$_2$O$_3$(10$\bar{1}$0) and $α$-(In$_x$Ga$_{1-x}$)$_2$O$_3$(10$\bar{1}$0) thin films are realized. The presence of In on the $α$-Ga$_2$O$_3$ growth surface remarkably expands its growth window far into the metal-rich flux regime and to higher growth temperatures. With increasing O-to-Ga flux ratio ($R_{\text{O}}$), In incorporates into $α$-(In$_x$Ga$_{1-x}$)$_2$O$_3$ up to $x \leq 0.08$. Upon a critical thickness, $β$-(In$_x$Ga$_{1-x}$)$_2$O$_3$ nucleates and subsequently heteroepitaxially grows on top of $α$-(In$_x$Ga$_{1-x}$)$_2$O$_3$ facets. Metal-rich MOCATAXY growth conditions, where $α$-Ga$_2$O$_3$ would not conventionally stabilize, lead to single-crystalline $α$-Ga$_2$O$_3$ with negligible In incorporation and improved surface morphology. Higher $T_{\text{G}}$ further results in single-crystalline $α$-Ga$_2$O$_3$ with well-defined terraces and step edges at their surfaces. For $R_{\text{O}} \leq 0.53$, In acts as a surfactant on the $α$-Ga$_2$O$_3$ growth surface by favoring step edges, while for $R_{\text{O}} \geq 0.8$, In incorporates and leads to a-plane $α$-(In$_x$Ga$_{1-x}$)$_2$O$_3$ faceting and the subsequent ($\bar{2}$01) $β$-(In$_x$Ga$_{1-x}$)$_2$O$_3$ growth on top. Thin film analysis by STEM reveals highly crystalline $α$-Ga$_2$O$_3$ layers and interfaces. We provide a phase diagram to guide the MBE and MOCATAXY growth of single-crystalline $α$-Ga$_2$O$_3$ on $α$-Al$_2$O$_3$(10$\bar{1}$0).

cond-mat.mtrl-sci↗

arXiv2023

Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data

The structural characterization of hetero-aggregates in 3D is of great interest, e.g., for deriving process-structure or structure-property relationships. However, since 3D imaging techniques are often difficult to perform as well as time and cost intensive, a characterization of hetero-aggregates based on 2D image data is desirable, but often non-trivial. To overcome the issues of characterizing 3D structures from 2D measurements, a method is presented that relies on machine learning combined with methods of spatial stochastic modeling, where the latter are utilized for the generation of synthetic training data. This kind of training data has the advantage that time-consuming experiments for the synthesis of differently structured materials followed by their 3D imaging can be avoided. More precisely, a parametric stochastic 3D model is presented, from which a wide spectrum of virtual hetero-aggregates can be generated. Additionally, the virtual structures are passed to a physics-based simulation tool in order to generate virtual scanning transmission electron microscopy (STEM) images. The preset parameters of the 3D model together with the simulated STEM images serve as a database for the training of convolutional neural networks, which can be used to determine the parameters of the underlying 3D model and, consequently, to predict 3D structures of hetero-aggregates from 2D STEM images. Furthermore, an error analysis is performed to evaluate the prediction power of the trained neural networks with respect to structural descriptors, e.g. the hetero-coordination number.

cs.CV↗

arXiv2021

Live processing of momentum-resolved STEM data for first moment imaging and ptychography

A reformulated implementation of single-sideband ptychography enables analysis and display of live detector data streams in 4D scanning transmission electron microscopy (STEM) using the LiberTEM open-source platform. This is combined with live first moment and further virtual STEM detector analysis. Processing of both real experimental and simulated data shows the characteristics of this method when data is processed progressively, as opposed to the usual offline processing of a complete dataset. In particular, the single side band method is compared to other techniques such as the enhanced ptychographic engine in order to ascertain its capability for structural imaging at increased specimen thickness. Qualitatively interpretable live results are obtained also if the sample is moved, or magnification is changed during the analysis. This allows live optimization of instrument as well as specimen parameters during the analysis. The methodology is especially expected to improve contrast- and dose-efficient in-situ imaging of weakly scattering specimens, where fast live feedback during the experiment is required.

physics.data-an↗

arXiv2019

Electron Bessel beam diffraction for precise and accurate nanoscale strain mapping

Strain has a strong effect on the properties of materials and the performance of electronic devices. Their ever shrinking size translates into a constant demand for accurate and precise measurement methods with very high spatial resolution. In this regard, transmission electron microscopes are key instruments thanks to their ability to map strain with sub-nanometer resolution. Here we present a novel method to measure strain at the nanometer scale based on the diffraction of electron Bessel beams. We demonstrate that our method offers a strain sensitivity better than $2.5 \cdot 10^{-4}$ and an accuracy of $1.5 \cdot 10^{-3}$, competing with, or outperforming, the best existing methods with a simple and easy to use experimental setup.

cond-mat.mtrl-sci↗