Imaging interstitial iron concentrations in boron-doped crystalline silicon using photoluminescence

Imaging the band-to-band photoluminescence of silicon wafers is known to provide rapid and high-resolution images of the carrier lifetime. Here, we show that such photoluminescence images, taken before and after dissociation of iron-boron pairs, allow an accurate image of the interstitial iron concentration across a boron-doped p-type silicon wafer to be generated. Such iron images can be obtained more rapidly than with existing point-by-point iron mapping techniques. However, because the technique is best used at moderate illumination intensities, it is important to adopt a generalized analysis that takes account of different injection levels across a wafer. The technique has been verified via measurement of a deliberately contaminated single-crystal silicon wafer with a range of known iron concentrations. It has also been applied to directionally solidified ingot-grown multicrystalline silicon wafers made for solar cell production, which contain a detectible amount of unwanted iron. The iron images on these wafers reveal internal gettering of iron to grain boundaries and dislocated regions during ingot growth.D.M. is supported by an Australian Research Council QEII Fellowship. The Centre of Excellence for Advanced Silicon Photovoltaics and Photonics at UNSW is funded by the Australian Research Council

A new art code for tomographic interferometry

A new algebraic reconstruction technique (ART) code based on the iterative refinement method of least squares solution for tomographic reconstruction is presented. Accuracy and the convergence of the technique is evaluated through the application of numerically generated interferometric data. It was found that, in general, the accuracy of the results was superior to other reported techniques. The iterative method unconditionally converged to a solution for which the residual was minimum. The effects of increased data were studied. The inversion error was found to be a function of the input data error only. The convergence rate, on the other hand, was affected by all three parameters. Finally, the technique was applied to experimental data, and the results are reported

Periodic subvarieties of a projective variety under the action of a maximal rank abelian group of positive entropy

We determine positive-dimensional G-periodic proper subvarieties of an n-dimensional normal projective variety X under the action of an abelian group G of maximal rank n-1 and of positive entropy. The motivation of the paper is to understand the obstruction for X to be G-equivariant birational to the quotient variety of an abelian variety modulo the action of a finite group.Comment: Asian Journal of Mathematics (to appear), Special issue on the occasion of Prof N. Mok's 60th birthda

Seismological support for the metastable superplume model, sharp features, and phase changes within the lower mantle

Recently, a metastable thermal-chemical convection model was proposed to explain the African Superplume. Its bulk tabular shape remains relatively stable while its interior undergoes significant stirring with low-velocity conduits along its edges and down-welling near the middle. Here, we perform a mapping of chemistry and temperature into P and S velocity variations and replace a seismically derived structure with this hybrid model. Synthetic seismogram sections generated for this 2D model are then compared directly with corresponding seismic observations of P (P, PCP, and PKP) and S (S, SCS, and SKS) phases. These results explain the anticorrelation between the bulk velocity and shear velocity and the sharpness and level of SKS travel time delays. In addition, we present evidence for the existence of a D" triplication (a putative phase change) beneath the down-welling structure

Characterisation of FAD-family folds using a machine learning approach

Flavin adenine dinucleotide (FAD) and its derivatives play a crucial role in biological processes. They are major organic cofactors and electron carriers in both enzymatic activities and biochemical pathways. We have analysed the relationships between sequence and structure of FAD-containing proteins using a machine learning approach. Decision trees were generated using the C4.5 algorithm as a means of automatically generating rules from biological databases (TOPS, CATH and PDB). These rules were then used as background knowledge for an ILP system to characterise the four different classes of FAD-family folds classified in Dym and Eisenberg (2001). These FAD-family folds are: glutathione reductase (GR), ferredoxin reductase (FR), p-cresol methylhydroxylase (PCMH) and pyruvate oxidase (PO). Each FADfamily was characterised by a set of rules. The “knowledge patterns” generated from this approach are a set of rules containing conserved sequence motifs, secondary structure sequence elements and folding information. Every rule was then verified using statistical evaluation on the measured significance of each rule. We show that this machine learning approach is capable of learning and discovering interesting patterns from large biological databases and can generate “knowledge patterns” that characterise the FADcontaining proteins, and at the same time classify these proteins into four different families

Linear Transmission of Composite Gaussian Measurements over a Fading Channel under Delay Constraints

Delay constrained linear transmission (LT) strategies are considered for the transmission of composite Gaussian measurements over an additive white Gaussian noise fading channel under an average power constraint. If the channel state information (CSI) is known by both the encoder and decoder, the optimal LT scheme in terms of the average mean-square error distortion is characterized under a strict delay constraint, and a graphical interpretation of the optimal power allocation strategy is presented. Then, for general delay constraints, two LT strategies are proposed based on the solution to a particular multiple measurements-parallel channels scenario. It is shown that the distortion decreases as the delay constraint is relaxed, and when the delay constraint is completely removed, both strategies achieve the optimal performance under certain matching conditions. If the CSI is known only by the decoder, the optimal LT strategy is derived under a strict delay constraint. The extension to general delay constraints is elusive. As a first step towards understanding the structure of the optimal scheme in this case, it is shown that for the multiple measurementsparallel channels scenario, any LT scheme that uses only a oneto-one linear mapping between measurements and channels is suboptimal in general

Classification for the universal scaling of N\'eel temperature and staggered magnetization density of three-dimensional dimerized spin-1/2 antiferromagnets

Inspired by the recently theoretical development relevant to the experimental data of TlCuCl$_3$, particularly those associated with the universal scaling between the N\'eel temperature $T_N$ and the staggered magnetization density $M_s$, we carry a detailed investigation of 3-dimensional (3D) dimerized quantum antiferromagnets using the first principles quantum Monte Carlo calculations. The motivation behind our study is to better understand the microscopic effects on these scaling relations of $T_N$ and $M_s$, hence to shed some light on some of the observed inconsistency between the theoretical and the experimental results. Remarkably, for the considered 3D dimerized models, we find that the established universal scaling relations can indeed be categorized by the amount of stronger antiferromagnetic couplings connected to a lattice site. Convincing numerical evidence is provided to support this conjecture. The relevance of the outcomes presented here to the experiments of TlCuCl$_3$ is briefly discussed as well.Comment: 9 pages, 27 figure

Integrative machine learning approach for multi-class SCOP protein fold classification

Classification and prediction of protein structure has been a central research theme in structural bioinformatics. Due to the imbalanced distribution of proteins over multi SCOP classification, most discriminative machine learning suffers the well-known ‘False Positives ’ problem when learning over these types of problems. We have devised eKISS, an ensemble machine learning specifically designed to increase the coverage of positive examples when learning under multiclass imbalanced data sets. We have applied eKISS to classify 25 SCOP folds and show that our learning system improved over classical learning methods