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Register a new userCurrent Overview of Statistical Fiber Bundles Model and Its Application to Physics-based Reliability Analysis of Thin-film Dielectrics
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In this paper, we present a critical overview of statistical fiber bundles models. We discuss relevant aspects, like assumptions and consequences stemming from models in the literature and propose new ones. This is accomplished by concentrating on both the physical and statistical aspects of a specific load-sharing example, the breakdown (BD) for circuits of capacitors and related dielectrics. For series and parallel/series circuits (series/parallel reliability systems) of ordinary capacitors, the load-sharing rules are derived from the electrical laws. This with the BD formalism is then used to obtain the BD distribution of the circuit. The BD distribution and Gibbs measure are given for a series circuit and the size effects are illustrated for simulations of series and parallel/series circuits. This is related to the finite weakest link adjustments for the BD distribution that arise in large series/parallel reliability load-sharing systems, such as dielectric BD, from their extreme value approximations. An elementary but in-depth discussion of the physical aspects of SiO$_2$ and HfO$_2$ dielectrics and cell models is given. This is used to study a load-sharing cell model for the BD of HfO$_2$ dielectrics and the BD formalism. The latter study is based on an analysis of Kim and Lee (2004)s data for such dielectrics. Here, several BD distributions are compared in the analysis and proportional hazard regression models are used to study the BD formalism. In addition, some areas of open research are discussed.
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A fast physics analysis framework has been developed based on SNiPER to process the increasingly large data sample collected by BESIII. In this framework, a reconstructed event data model with SmartRef is designed to improve the speed of Input/Output operations, and necessary physics analysis tools are migrated from BOSS to SNiPER. A real physics analysis $e^{+}e^{-} rightarrow pi^{+}pi^{-}J/psi$ is used to test the new framework, and achieves a factor of 10.3 improvement in Input/Output speed compared to BOSS. Further tests show that the improvement is mainly attributed to the new reconstructed event data model and the lazy-loading functionality provided by SmartRef.
To understand the unexpectedly high thermoelectric performance observed in the thin-film Heusler alloy Fe$_2$V$_{0.8}$W$_{0.2}$Al, we study the magnon drag effect, generated by the tungsten based impurity band, as a possible source of this enhancement, in analogy to the phonon drag observed in FeSb$_2$. Assuming that the thin-film Heusler alloy has a conduction band integrating with the impurity band, originated by the tungsten substitution, we derive the electrical conductivity $L_{11}$ based on the self-consistent t-matrix approximation and the thermoelectric conductivity $L_{12}$ due to magnon drag, based on the linear response theory, and estimate the temperature dependent electrical resistivity, Seebeck coefficient and power factor. Finally, we compare the theoretical results with the experimental results of the thin-film Heusler alloy to show that the origin of the exceptional thermoelectric properties is likely to be due to the magnon drag related with the tungsten-based impurity band.
Asymptotic formulae for likelihood-based tests of new physics presents a mathematical formalism for a new approximation for hypothesis testing in high energy physics. The approximations are designed to greatly reduce the computational burden for such problems. We seek to test the conditions under which the approximations described remain valid. To do so, we perform parallel calculations for a range of scenarios and compare the full calculation to the approximations to determine the limits and robustness of the approximation. We compare this approximation against values calculated with the Collie framework, which for our analysis we assume produces true values.
Cycles, which can be found in many different kinds of networks, make the problems more intractable, especially when dealing with dynamical processes on networks. On the contrary, tree networks in which no cycle exists, are simplifications and usually allow for analyticity. There lacks a quantity, however, to tell the ratio of cycles which determines the extent of network being close to tree networks. Therefore we introduce the term Cycle Nodes Ratio (CNR) to describe the ratio of number of nodes belonging to cycles to the number of total nodes, and provide an algorithm to calculate CNR. CNR is studied in both network models and real networks. The CNR remains unchanged in different sized Erdos Renyi (ER) networks with the same average degree, and increases with the average degree, which yields a critical turning point. The approximate analytical solutions of CNR in ER networks are given, which fits the simulations well. Furthermore, the difference between CNR and two-core ratio (TCR) is analyzed. The critical phenomenon is explored by analysing the giant component of networks. We compare the CNR in network models and real networks, and find the latter is generally smaller. Combining the coarse-graining method can distinguish the CNR structure of networks with high average degree. The CNR is also applied to four different kinds of transportation networks and fungal networks, which give rise to different zones of effect. It is interesting to see that CNR is very useful in network recognition of machine learning.
This paper presents the preliminary results of optical characterization using spectroscopic ellipsometry of wurtzite indium gallium nitride (InxGa1-xN) thin films with medium indium content (0.38<x<0.68) that were deposited on silicon dioxide using plasma-enhanced evaporation. A Kramers-Kronig consistent parametric analytical model using Gaussian oscillators to describe the absorption spectra has been developed to extract the real and imaginary components of the dielectric function ({epsilon}1, {epsilon}2) of InxGa1-xN films. Scanning electron microscope (SEM) images are presented to examine film microstructure and verify film thicknesses determined from ellipsometry modelling. This fitting procedure, model, and parameters can be employed in the future to extract physical parameters from ellipsometric data from other InxGa1-xN films.
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