Wet Chemical Treatment and Mg Doping of p-InP Surfaces for Ohmic Low-Resistive Metal Contacts

Manufacturing a low-resistive Ohmic metal contact on p-type InP crystals for various applications is a challenge because of the Fermi-level pinning via surface defects and the diffusion of p-type doping atoms in InP. Development of wet-chemistry treatments and nanoscale control of p-doping for InP surfaces is crucial for decreasing the device resistivity losses and durability problems. Herein, a proper combination of HCl-based solution immersion, which directly provides an unusual wet chemical-induced InP(100)c(2 × 2) atomic structure, and low-temperature Mg-surface doping of the cleaned InP before Ni-film deposition is demonstrated to decrease the contact resistivity of Ni/p-InP by the factor of 10 approximately as compared to the lowest reference value without Mg. Deposition of the Mg intermediate layer on p-InP and postheating of Mg/p-InP at 350 °C, both performed in ultrahigh-vacuum (UHV) chamber, lead to intermixing of Mg and InP elements according to X-ray photoelectron spectroscopy. Introducing a small oxygen gas background (O₂ ≈ 10⁻⁶ mbar) in UHV chamber during the postheating of Mg/p-InP enhances the indium outdiffusion and provides the lowest contact resistivity. Quantum mechanical simulations indicate that the presence of InP native oxide or/and metal indium alloy at the interface increases In diffusion.     

Knowledge based quality-driven architecture design and evaluation

Modelling and evaluating quality properties of software is of high importance, especially when our every day life depends on the quality of services produced by systems and devices embedded into our surroundings. This paper contributes to the body of research in quality and model driven software engineering. It does so by introducing; (1) a quality aware software architecting approach and (2) a supporting tool chain. The novel approach with supporting tools enables the systematic development of high quality software by merging benefits of knowledge modelling and management, and model driven architecture design enhanced with domain-specific quality attributes. The whole design flow of software engineering is semi-automatic; specifying quality requirements, transforming quality requirements to architecture design, representing quality properties in architectural models, predicting quality fulfilment from architectural models, and finally, measuring quality aspects from implemented source code. The semi-automatic design flow is exemplified by the ongoing development of a secure middleware for peer-to-peer embedded systems.     

Checking determinism of XML Schema content models in optimal time

We consider the determinism checking of XML Schema content models, as required by the W3C Recommendation. We argue that currently applied solutions have flaws and make processors vulnerable to exponential resource needs by pathological schemas, and we help to eliminate this potential vulnerability of XML Schema based systems. XML Schema content models are essentially regular expressions extended with numeric occurrence indicators. A previously published polynomial-time solution to check the determinism of such expressions is improved to run in linear time, and the improved algorithm is implemented and evaluated experimentally. When compared to the corresponding method of a popular production-quality XML Schema processor, the new implementation runs orders of magnitude faster. Enhancing the solution to take further extensions of XML Schema into account without compromising its linear scalability is also discussed.     

Bayesian Clustering of Fuzzy Feature Vectors Using a Quasi-Likelihood Approach

Bayesian model-based classifiers, both unsupervised and supervised, have been studied extensively and their value and versatility have been demonstrated on a wide spectrum of applications within science and engineering. A majority of the classifiers are built on the assumption of intrinsic discreteness of the considered data features or on the discretization of them prior to the modeling. On the other hand, Gaussian mixture classifiers have also been utilized to a large extent for continuous features in the Bayesian framework. Often the primary reason for discretization in the classification context is the simplification of the analytical and numerical properties of the models. However, the discretization can be problematic due to its textit{ad hoc} nature and the decreased statistical power to detect the correct classes in the resulting procedure. We introduce an unsupervised classification approach for fuzzy feature vectors that utilizes a discrete model structure while preserving the continuous characteristics of data. This is achieved by replacing the ordinary likelihood by a binomial quasi-likelihood to yield an analytical expression for the posterior probability of a given clustering solution. The resulting model can be justified from an information-theoretic perspective. Our method is shown to yield highly accurate clusterings for challenging synthetic and empirical data sets.     

Realistic eight-port homodyne detection and covariant phase space observables

We consider the quantum optical eight-port homodyne detection scheme in the case that each of the associated photon detectors is assigned with a different quantum efficiency. We give a mathematically rigorous and strictly quantum mechanical proof of the fact that the measured observable (positive operator measure) in the high-amplitude limit is a smearing of the covariant phase space observable related to the ideal measurement, that is, the measurement performed with fully efficient detectors. The result is proved for an arbitary parameter field. Furthermore, we investigate some properties of the measured observable. In particular, we show that detector inefficiencies do not affect the observable's ability to distinguish between different states.     

Environmental Control of Triplet Emission in Donor–Bridge–Acceptor Organometallics

Carbene‐metal‐amides (CMAs) are a promising family of donor–bridge–acceptor molecular charge‐transfer (CT) emitters for organic light‐emitting diodes. A universal approach is demonstrated to tune the energy of their CT emission. A blueshift of up to 210 meV is achievable in solid state via dilution in a polar host matrix. The origin of this shift has two components: constraint of thermally‐activated triplet diffusion, and electrostatic interactions between guest and polar host. This allows the emission of mid‐green CMA archetypes to be tuned to sky blue without chemical modifications. Monte‐Carlo simulations based on a Marcus‐type transfer integral successfully reproduce the concentration‐ and temperature‐dependent triplet diffusion process, revealing a substantial shift in the ensemble density of states in polar hosts. In gold‐bridged CMAs, this shift does not lead to a significant change in luminescence lifetime, thermal activation energy, reorganization energy, or intersystem crossing rate. These discoveries offer new insight into coupling between the singlet and triplet manifolds in CMA materials, revealing a dominant interaction between states of CT character. The same approach is employed using materials which have been chemically modified to alter the energy of their CT state directly, shifting the emission of sky‐blue chromophores into the practical blue range.     

The Liquidus in Cu–O–CaO System at Metallic Copper Saturation up to 1698 K

We present experimental results for the liquidus of the pseudo binary system Cu₂O–CaO at metallic copper saturation up to 1698 K in steps of 25 K. In this study, we quantified the liquidus (on the CaO side of the binary) by the equilibration, quench, and analysis technique, with Cu₂O, CaO, and Cu as the only primary crystalline phases. Experiments were conducted in an atmosphere of pure argon up to the critical temperature (1618 K) in the Cu–O system. Above this temperature, experiments were performed at a fixed oxygen pressure of (P_O2 = 1.01 kPa). Equilibrated specimens were quenched in ice‐cold water. We then quantified the liquid chemical compositions by means of energy‐dispersive X‐ray spectroscopy and electron probe micro‐analyzer. Results from this study reveal that below the critical point, some previous studies have overestimated CaO solubility amounts in the molten phase of the system, by up to 4.35 wt% CaO. Thermodynamic modeling of systems containing this binary should therefore also account for this variance.