The effects of a thin film dopant precursor on the structure and properties of femtosecond-laser irradiated silicon

Femtosecond (fs) laser irradiation of a silicon substrate coated with a thin film is a flexible approach to producing metastable alloys with unique properties, including near-unity sub-band gap absorptance extending into the infrared. However, dopant incorporation from a thin film during fs-laser irradiation is not well understood. We study the thin film femtosecond-laser doping process through optical and structural characterization of silicon fs-laser doped using a selenium thin film, and compare the resulting microstructure and dopant distribution to fs-laser doping with sulfur from a gaseous precursor. We show that a thin film dopant precursor significantly changes the laser-material interactions, modifying both the surface structuring and dopant incorporation processes and in turn affecting p–n diode behavior.     

Global and local behavior of zeros of nonpositive type

A generalized Nevanlinna function Q(z)$Q(z)$ with one negative square has precisely one generalized zero of nonpositive type in the closed extended upper halfplane. The fractional linear transformation defined by _Qτ(z)=(Q(z)−τ)/(1+τQ(z))$Q_{\tau }(z)=(Q(z)-\tau )/(1+\tau Q(z))$, τ∈R∪{∞}$\tau \in \mathbb{R}\cup \{\infty \}$, is a generalized Nevanlinna function with one negative square. Its generalized zero of nonpositive type α(τ)$\alpha (\tau )$ as a function of τ is being studied. In particular, it is shown that it is continuous and its behavior in the points where the function extends through the real line is investigated.     

Optimality and Greed in Dynamic Allocation

In dynamic allocation items arrive and depart randomly and while present are stored in a limited facility; the job of an allocation algorithm is to decide whether and where to store an arriving item, while trying to minimize the cost incurred by rejections. Ordinarily, to prove the value of such an algorithm, one must either assume a specific arrival distribution (e.g., Poisson-λ), or try to obtain bounds relative to an adversary (as in competitive analysis).We present here a novel method of proving precise optimality for certain kinds of dynamic allocation algorithms, without assuming a specific arrival distribution. We do need to assume memoryless departure for at least some item types, and most importantly, we must assume conditions are such that it is not right to reject arrivals unnecessarily.The method is applied to two simple call assignment problems which arose, in quite different circumstances, at Lucent Technologies. In both cases the method was successful in showing that the intuitively best assignment algorithm really does minimize expected cost under fairly general, and essentially necessary, assumptions.     

Liquid-Phase Epitaxy of (Hg,Cd)Te on CdTe Substrates by a Vertical Dipping Technique

Hg_1−xCd_xTe layers on CdTe substrates were grown from Te-rich melt solutions by a vertical dipping technique using a special quasi-closed system with ground-glass sealing. Results are good reproducibilities of the electrical properties after annealing in Hg-rich atmosphere (p77 ≈ 2 · 10¹⁶ cm⁻³ μ77 ≈ 500 cm² V⁻¹ s⁻¹) and of the x-value, respectively. A horizontal position of the substrate downwards to the melt solution yields, in difference to a vertical one, to homogeneous layer thicknesses. Short meltback steps before growth leads to sharper profiles of composition.     

Mössbauer,EPR and susceptibility studies of highly conducting polypyrrole doped with iron chloride

The results of Mössbauer, EPR and static magnetic susceptibility measurements on polypyrrole doped with FeCl₃ are presented. They indicate that iron chloride tetrahedra are interacting with the polymer matrix by hydrogen bonding. This interaction increases with dehydration of the samples. The susceptibility showed a maximum near 5 K, which is an indication for antiferromagnetic interaction, probably between paramagnetic iron and polarons.     

Advanced Genetic Programming Based Machine Learning

A Genetic Programming based approach for solving classification problems is presented in this paper. Classification is understood as the act of placing an object into a set of categories, based on the object’s properties; classification algorithms are designed to learn a function which maps a vector of object features into one of several classes. This is done by analyzing a set of input-output examples (“training samples”) of the function. Here we present a method based on the theory of Genetic Algorithms and Genetic Programming that interprets classification problems as optimization problems: Each presented instance of the classification problem is interpreted as an instance of an optimization problem, and a solution is found by a heuristic optimization algorithm. The major new aspects presented in this paper are advanced algorithmic concepts as well as suitable genetic operators for this problem class (mainly the creation of new hypotheses by merging already existing ones and their detailed evaluation). The experimental part of the paper documents the results produced using new hybrid variants of Genetic Algorithms as well as investigated parameter settings. Graphical analysis is done using a novel multiclass classifier analysis concept based on the theory of Receiver Operating Characteristic curves. The work described in this paper was done within the Translational Research Project L282 “GP-Based Techniques for the Design of Virtual Sensors” sponsored by the Austrian Science Fund (FWF).     

Convergence Rate for a Parabolic Equation with Supercritical Nonlinearity

We study the behavior of solutions of the Cauchy problem for a diffusion equation with supercritical nonlinearity. It is shown that if two solutions are initially close enough near the spatial infinity, then these solutions approach each other. Under some conditions, we determine the exact convergence rate, which turns out to depend on initial data.     

Die Anwendung des Magnesiums als Reagens

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