Heteroepitaxy of semiconductor-insulator layers and their interface properties

The epitaxial growth of Ba₂SiO₄ thin films on Si(001) by co-deposition of Ba and Si in an oxygen background pressure is systematically investigated with a focus on the epitaxial interface. A structural investigation is performed by employing x-ray photoelectron spectroscopy (XPS), low energy electron diffraction (LEED) and aberration-corrected scanning transmission electron microscopy (STEM). In addition, an electrical characterization is done using MOS test capacitors. The stoichiometry at the interface turns out to be critically dependent on the oxygen background pressure during deposition. Films grown with an oxygen pressure just above the saturation point for a complete oxidation of the film still feature 1/4 ML of O atoms in Si-O-Si bonding states. In comparison, the Ba₂SiO₄ bulk structure has only O atoms in Si-O-Ba bonding states. STEM shows that these films form an atomically sharp interface to Si(001) and that the Ba₂SiO₄ bulk structure is maintained up to the penultimate layer at the interface. Only one silicate layer is changed to a (2x3) structure, which is also observed in LEED, to match the (2x1.5) bulk structure to Si(001), neglecting relaxations. An interface model is proposed for these films, which features a pseudo-(2x1) reconstruction of the Si surface and helps to understand the formation process of the epitaxial interface in greater detail. The growth in a high oxygen pressure leads to the formation of Si-rich silicate at the interface, which does not prevent the epitaxial growth but modifies the interface into a (2x6) structure. Moreover, a Ba surplus results in the formation of interfacial silicide, which is characterized by a (4x2) structure. A dielectric constant of k=22.5 ± 1.1 is found for Ba₂SiO₄, as well as band offsets to Si(001) larger than 1.8 eV for crystalline layers. Moreover, leakage current densities as low as 2 ⋅ 10⁻⁶ A/cm² at -1 V are measured for a 10 nm thick film. Interface trap densities at midgap of (1.14 ± 0.78) ⋅ 10¹² eV⁻¹cm⁻¹² are measured for crystalline films with an abrupt interface. Amorphous films show slightly higher interface trap densities of (2.72 ± 0.82) ⋅ 10¹² eV⁻¹cm⁻¹² at midgap. A further reduction of the interface trap density is possible by incorporating a Si-rich silicate layer at the interface, which results in interface trap densities of (3.32 ± 0.45) ⋅ 10¹¹ eV⁻¹cm⁻¹² at midgap for crystalline layers. However, even though no SiO₂ forms at the interface, the epitaxial interface still contributes an offset of (0.56 ± 0.08) nm to the overall CET, which greatly limits the achievable minimum CET of the gate stack

Citizen science:A new perspective to advance spatial pattern evaluation in hydrology

Citizen science opens new pathways that can complement traditional scientific practice. Intuition and reasoning often make humans more effective than computer algorithms in various realms of problem solving. In particular, a simple visual comparison of spatial patterns is a task where humans are often considered to be more reliable than computer algorithms. However, in practice, science still largely depends on computer based solutions, which inevitably gives benefits such as speed and the possibility to automatize processes. However, the human vision can be harnessed to evaluate the reliability of algorithms which are tailored to quantify similarity in spatial patterns. We established a citizen science project to employ the human perception to rate similarity and dissimilarity between simulated spatial patterns of several scenarios of a hydrological catchment model. In total, the turnout counts more than 2500 volunteers that provided over 43000 classifications of 1095 individual subjects. We investigate the capability of a set of advanced statistical performance metrics to mimic the human perception to distinguish between similarity and dissimilarity. Results suggest that more complex metrics are not necessarily better at emulating the human perception, but clearly provide auxiliary information that is valuable for model diagnostics. The metrics clearly differ in their ability to unambiguously distinguish between similar and dissimilar patterns which is regarded a key feature of a reliable metric. The obtained dataset can provide an insightful benchmark to the community to test novel spatial metrics

Long short-term memory networks enhance rainfall-runoff modelling at the national scale of Denmark

This study explores the application of long short-term memory (LSTM) networks to simulate runoff at the national scale of Denmark using data from 301 catchments. This is the first LSTM application on Danish data. The results were benchmarked against the Danish national water resources model (DK-model), a physically based hydrological model. The median Kling-Gupta Efficiency (KGE), a common metric to assess performance of runoff predictions (optimum of 1), increased from 0.7 (DK-model) to 0.8 (LSTM) when trained against all catchments. Overall, the LSTM outperformed the DK-model in 80% of catchments. Despite the compelling KGE evaluation, the water balance closure was modelled less accurately by the LSTM. The applicability of LSTM networks for modelling ungauged catchments was assessed via a spatial split-sample experiment. A 20% spatial hold-out showed poorer performance of the LSTM with respect to the DK model. However, after pre-training, that is, weight initialisation obtained from training against simulated data from the DK-model, the performance of the LSTM was effectively improved. This formed a convincing argument supporting the knowledge-guided machine learning (ML) paradigm to integrate physically based models and ML to train robust models that generalise well

Problems of Nonidentifiability and Alternative Solutions

The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example

Surface state conductivity in epitaxially grown Bi1-xSbx(111) films

Topologically non-trivial surface states were reported first on ${\mathrm{Bi}}_{1-x}$Sb x bulk crystals. In this study we present transport measurements performed on thin ${\mathrm{Bi}}_{1-x}$Sb x -films (up to 24 nm thickness) grown epitaxially on Si(111) with various Sb-concentrations (up to x = 0.22). The analysis of the temperature dependency allowed us to distinguish between different transport channels originating from surface and bulk bands as well as impurity states. At temperatures below 30 K the transport is mediated by surface states while at higher temperatures activated transport via bulk channels sets in. The surface state conductivity and bulk band gaps can be tuned by the Sb-concentration and film thickness, respectively. For films as thin as 4 nm the surface state transport is strongly suppressed in contrast to Bi(111) films grown under identical conditions. The impurity channel is of intrinsic origin due to the growth and alloy formation process and turns out to be located at the buried interface.DF

Robotic Process Flexibilization in the Term of Crisis: A Case Study of Robotic Process Automation in a Public Health Department

Due to the pandemic, institutions of the health sector, especially public health departments, are facing major challenges in managing their processes. In a constantly changing environment, new and existing processes have to be adopted or implemented in the shortest possible time, while the process volumes to be managed are constantly increasing. In our article, we use a case study to show how the concept of “flexibility by design” can be influenced by RPA in the sensitive environment of healthcare and how exactly flexibility in process execution can be achieved with it. As a result, we show that RPA can positively implement or enable three of the six realization options from the concept. In addition, the concept was supplemented by two aggregated theoretical dimensions, namely “Response” and “Range,” which summarize the supporting conditions for a process flexibilization with RPA. In the article, we thereby show how exactly RPA can complement existing processes in a healthcare environment and thus, serve to subsequently make rigid process models more flexible

A Curriculum Mining Method for Clustering Study Modules and Assessing their Uniqueness

Curriculum development can pursue several pedagogical goals. One is to design a curriculum that is attractive in comparison with other competing universities. To contribute to such a comparative assessment and, thereby, to the targeted development of curricula, the idea of a curriculum mining method is presented. Here, study modules are divided into homogeneous groups by means of a document clustering procedure. The generated knowledge improves the comparative assessment of curricula in two ways: First, depending on the context, it can be used to assess either the extent of the uniqueness of a module or its consecutiveness with other modules. Second, by supplementing the modules with metadata (e.g., region), a competitive analysis is provided in terms of modules offered by competing institutions. An exemplary case study demonstrates how this improves the evaluation of a specific IS curriculum. In conclusion, the current limitations and next steps of the research project are summarized

The Fear of Losing Control - What Prevents the Automation of Business Processes in Sensitive Areas

This article explores the potential barriers and drivers of end-user adoption of robotic process automation (RPA) technology in particularly sensitive process areas. For this purpose, the grounded theory method was used within a health authority to determine which factors influence the intention to use and the benefits of such solutions. RPA enables the automation of repetitive and rule-based processes. The development and usage experiences of the respective employees as users of the technology were recorded and used for conceptualization. These found constructs were then compared with those from the established scientific literature. The results show that the obvious drivers can be described in terms of "transparency" and "explainability" and that these are novelty factors compared to established RPA-specific success factors from the relevant literature