Hydrogen refinement during solid phase epitaxy of buried amorphous silicon layers

The effect of hydrogen on the kinetics of solid phase epitaxy (SPE) have been studied in buried amorphous Si layers. The crystallization rate of the front amorphous/crystalline (a/c) interface is monitored with time resolved reflectivity.Secondary ion mass spectrometry(SIMS) is used to examine H implanted profiles at selected stages of the anneals. The H retardation of the SPE rate is determined up to a H concentration of 2.3×10²⁰ cm¯³ where the SPE rate decreases by 80%. Numerical simulations are performed to model the H diffusion, the moving a/c interfaces and the refinement of the H profile at these interfaces. Despite the high H concentration involved, a simple Fickian diffusion model results in good agreement with the SIMS data. The segregation coefficient is estimated to be 0.07 at 575 °C. A significant fraction of the H escapes from the a-Si layer during SPE especially once the two a/c interfaces meet which is signified by the lack of H-related voids after a subsequent high temperature anneal.This research was supported by a grant from the Australian Research Council

The effect of low-energy ion-implantation on the electrical transport properties of Si-SiO2 MOSFETs

Using silicon MOSFETs with thin (5nm) thermally grown SiO2 gate dielectrics, we characterize the density of electrically active traps at low-temperature after 16keV phosphorus ion-implantation through the oxide. We find that, after rapid thermal annealing at 1000oC for 5 seconds, each implanted P ion contributes an additional 0.08 plus/minus 0.03 electrically active traps, whilst no increase in the number of traps is seen for comparable silicon implants. This result shows that the additional traps are ionized P donors, and not damage due to the implantation process. We also find, using the room temperature threshold voltage shift, that the electrical activation of donors at an implant density of 2x10^12 cm^-2 is ~100%.Comment: 11 pages, 10 figure

Correlation Clustering with Low-Rank Matrices

Correlation clustering is a technique for aggregating data based on qualitative information about which pairs of objects are labeled 'similar' or 'dissimilar.' Because the optimization problem is NP-hard, much of the previous literature focuses on finding approximation algorithms. In this paper we explore how to solve the correlation clustering objective exactly when the data to be clustered can be represented by a low-rank matrix. We prove in particular that correlation clustering can be solved in polynomial time when the underlying matrix is positive semidefinite with small constant rank, but that the task remains NP-hard in the presence of even one negative eigenvalue. Based on our theoretical results, we develop an algorithm for efficiently "solving" low-rank positive semidefinite correlation clustering by employing a procedure for zonotope vertex enumeration. We demonstrate the effectiveness and speed of our algorithm by using it to solve several clustering problems on both synthetic and real-world data

Electrically-detected magnetic resonance in ion-implanted Si:P nanostructures

We present the results of electrically-detected magnetic resonance (EDMR) experiments on silicon with ion-implanted phosphorus nanostructures, performed at 5 K. The devices consist of high-dose implanted metallic leads with a square gap, into which Phosphorus is implanted at a non-metallic dose corresponding to 10^17 cm^-3. By restricting this secondary implant to a 100 nm x 100 nm region, the EDMR signal from less than 100 donors is detected. This technique provides a pathway to the study of single donor spins in semiconductors, which is relevant to a number of proposals for quantum information processing.Comment: 9 pages, 3 figure

Bayesian performance comparison of text classifiers

How can we know whether one classifier is really better than the other? In the area of text classification, since the publication of Yang and Liu's seminal SIGIR-1999 paper, it has become a standard practice for researchers to apply null-hypothesis significance testing (NHST) on their experimental results in order to establish the superiority of a classifier. However, such a frequentist approach has a number of inherent deficiencies and limitations, e.g., the inability to accept the null hypothesis (that the two classifiers perform equally well), the difficulty to compare commonly-used multivariate performance measures like F1 scores instead of accuracy, and so on. In this paper, we propose a novel Bayesian approach to the performance comparison of text classifiers, and argue its advantages over the traditional frequentist approach based on t-test etc. In contrast to the existing probabilistic model for F1 scores which is unpaired, our proposed model takes the correlation between classifiers into account and thus achieves greater statistical power. Using several typical text classification algorithms and a benchmark dataset, we demonstrate that the our approach provides rich information about the difference between two classifiers' performances

Magnetic and thermal properties of the S = 1/2 zig-zag spin-chain compound In2VO5

Static magnetic susceptibility \chi, ac susceptibility \chi_{ac} and specific heat C versus temperature T measurements on polycrystalline samples of In2VO5 and \chi and C versus T measurements on the isostructural, nonmagnetic compound In2TiO5 are reported. A Curie-Wiess fit to the \chi(T) data above 175 K for In2VO5 indicates ferromagnetic exchange between V^{4+} (S = 1/2) moments. Below 150 K the \chi(T) data deviate from the Curie-Weiss behavior but there is no signature of any long range magnetic order down to 1.8 K. There is a cusp at 2.8 K in the zero field cooled (ZFC) \chi(T) data measured in a magnetic field of 100 Oe and the ZFC and field cooled (FC) data show a bifurcation below this temperature. The frequency dependence of the \chi_{ac}(T) data indicate that below 3 K the system is in a spin-glass state. The difference \Delta C between the heat capacity of In2VO5 and In2TiO5 shows a broad anomaly peaked at 130 K. The entropy upto 300 K is more than what is expected for S = 1/2 moments. The anomaly in \Delta C and the extra entropy suggests that there may be a structural change below 130 K in In2VO5.Comment: 6 pages, 7 figures, 1 tabl

Phase transitions and iron-ordered moment form factor in LaFeAsO

Elastic neutron scattering studies of an optimized LaFeAsO single crystal reveal that upon cooling, an onset of the tetragonal (T)-to-orthorhombic (O) structural transition occurs at $T_\texttt{S} \approx 156$ K, and it exhibits a sharp transition at $T_\texttt{P} \approx 148$ K. We argue that in the temperature range $T_\texttt{S}$ to $T_\texttt{P}$, T and O structures may dynamically coexist possibly due to nematic spin correlations recently proposed for the iron pnictides, and we attribute $T_\texttt{P}$ to the formation of long-range O domains from the finite local precursors. The antiferromagnetic structure emerges at $T_\texttt{N} \approx 140$ K, with the iron moment direction along the O \emph{a} axis. We extract the iron magnetic form factor and use the tabulated $\langle j_0\rangle$ of Fe, Fe$^{2+}$ and Fe$^{3+}$ to obtain a magnetic moment size of $\sim$0.8 $\mu_\texttt{B}$ at 9.5 K.Comment: 7 pages, 6 figures, 3 table

High superconducting anisotropy and weak vortex pinning in Co doped LaFeAsO

Here, we present an electrical transport study in single crystals of LaFe$_{0.92}$Co$_{0.08}$AsO ($T_c \simeq 9.1$ K) under high magnetic fields. In contrast to most of the previously reported Fe based superconductors, and despite its relatively low $T_c$, LaFe$_{1-x}$Co$_x$AsO shows a superconducting anisotropy which is comparable to those seen for instance in the cuprates or $\gamma_H = H_{c2}^{ab}/H_{c2}^{c} = m_c/m_{ab} \simeq 9$, where $m_c/m_{ab}$ is the effective mass anisotropy. Although, in the present case and as in all Fe based superconductors, $\gamma \rightarrow 1$ as $T \rightarrow 0$. Under the application of an external field, we also observe a remarkable broadening of the superconducting transition particularly for fields applied along the inter-planar direction. Both observations indicate that the low dimensionality of LaFe$_{1-x}$Co$_x$AsO is likely to lead to a more complex vortex phase-diagram when compared to the other Fe arsenides and consequently, to a pronounced dissipation associated with the movement of vortices in a possible vortex liquid phase. When compared to, for instance, F-doped compounds pertaining to same family, we obtain rather small activation energies for the motion of vortices. This suggests that the disorder introduced by doping LaFeAsO with F is more effective in pinning the vortices than alloying it with Co.Comment: 7 figures, 7 pages, Phys. Rev. B (in press

Truth table invariant cylindrical algebraic decomposition

When using cylindrical algebraic decomposition (CAD) to solve a problem with respect to a set of polynomials, it is likely not the signs of those polynomials that are of paramount importance but rather the truth values of certain quantifier free formulae involving them. This observation motivates our article and definition of a Truth Table Invariant CAD (TTICAD). In ISSAC 2013 the current authors presented an algorithm that can efficiently and directly construct a TTICAD for a list of formulae in which each has an equational constraint. This was achieved by generalising McCallum's theory of reduced projection operators. In this paper we present an extended version of our theory which can be applied to an arbitrary list of formulae, achieving savings if at least one has an equational constraint. We also explain how the theory of reduced projection operators can allow for further improvements to the lifting phase of CAD algorithms, even in the context of a single equational constraint. The algorithm is implemented fully in Maple and we present both promising results from experimentation and a complexity analysis showing the benefits of our contributions.Comment: 40 page