Dislocation generation and propagation during flash lamp annealing

Dislocation generation and propagation during flash lamp annealing for 20 ms was investigated using wafers with sawed, ground, and etched surfaces. Due to the thermal stress resulting from the temperature profiles generated by the flash pre-existing dislocations propagate into the wafer from both surfaces during flash lamp annealing. A dislocation free zone was observed around 700 μm depth below the surface of a 900 μm thick sawed wafer. The dislocation propagation can be well described by a three-dimensional mechanical model. It was further demonstrated that in wafers being initially free of dislocations no dislocations are generated during flash lamp annealing

On the Impact of Strained PECVD Nitride Layers on Oxide Precipitate Nucleation in Silicon

PECVD nitride layers with different layer stress ranging from about 315 MPa to −1735 MPa were deposited on silicon wafers with similar concentration of interstitial oxygen. After a thermal treatment consisting of nucleation at 650°C for 4 h or 8 h followed annealing 780°C 3 h + 1000°C 16 h in nitrogen, the profiles of the oxide precipitate density were investigated. The binding states of hydrogen in the layers was investigated by FTIR. There is a clear effect of the layer stress on oxide precipitate nucleation. The higher the compressive layer stress is the higher is a BMD peak below the front surface. If the nitride layer is removed after the nucleation anneal the BMD peak below the front surface becomes lower. It is possible to model the BMD peak below the surface by vacancy in-diffusion from the silicon/nitride interface. With increasing duration of the nucleation anneal the vacancy injection from the silicon/nitride interface decreases and with increasing compressive layer stress it increases. © The Author(s) 2019

Editors' Choice - Precipitation of Suboxides in Silicon, their Role in Gettering of Copper Impurities and Carrier Recombination

This paper describes a theoretical investigation of the phase composition of oxide precipitates and the corresponding emission of self-interstitials at the minimum of the free energy and their evolution with increasing number of oxygen atoms in the precipitates. The results can explain the compositional evolution of oxide precipitates and the role of self-interstitials therein. The formation of suboxides at the edges of SiO2 precipitates after reaching a critical size can explain several phenomena like gettering of Cu by segregation to the suboxide region and lifetime reduction by recombination of minority carriers in the suboxide. It provides an alternative explanation, based on minimized free energy, to the theory of strained and unstrained plates. A second emphasis was payed to the evolution of the morphology of oxide precipitates. Based on the comparison with results from scanning transmission electron microscopy the sequence of morphology evolution of oxide precipitates was deduced. It turned out that it is opposite to the sequence assumed until now. © 2020 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited

Quantum picturalism for topological cluster-state computing

Topological quantum computing is a way of allowing precise quantum computations to run on noisy and imperfect hardware. One implementation uses surface codes created by forming defects in a highly-entangled cluster state. Such a method of computing is a leading candidate for large-scale quantum computing. However, there has been a lack of sufficiently powerful high-level languages to describe computing in this form without resorting to single-qubit operations, which quickly become prohibitively complex as the system size increases. In this paper we apply the category-theoretic work of Abramsky and Coecke to the topological cluster-state model of quantum computing to give a high-level graphical language that enables direct translation between quantum processes and physical patterns of measurement in a computer - a "compiler language". We give the equivalence between the graphical and topological information flows, and show the applicable rewrite algebra for this computing model. We show that this gives us a native graphical language for the design and analysis of topological quantum algorithms, and finish by discussing the possibilities for automating this process on a large scale.Comment: 18 pages, 21 figures. Published in New J. Phys. special issue on topological quantum computin

Critical wetting of a class of nonequilibrium interfaces: A mean-field picture

A self-consistent mean-field method is used to study critical wetting transitions under nonequilibrium conditions by analyzing Kardar-Parisi-Zhang (KPZ) interfaces in the presence of a bounding substrate. In the case of positive KPZ nonlinearity a single (Gaussian) regime is found. On the contrary, interfaces corresponding to negative nonlinearities lead to three different regimes of critical behavior for the surface order-parameter: (i) a trivial Gaussian regime, (ii) a weak-fluctuation regime with a trivially located critical point and nontrivial exponents, and (iii) a highly non-trivial strong-fluctuation regime, for which we provide a full solution by finding the zeros of parabolic-cylinder functions. These analytical results are also verified by solving numerically the self-consistent equation in each case. Analogies with and differences from equilibrium critical wetting as well as nonequilibrium complete wetting are also discussed.Comment: 11 pages, 2 figure

Implementation of Web-Based Respondent-Driven Sampling among Men who Have Sex with Men in Vietnam

Objective: Lack of representative data about hidden groups, like men who have sex with men (MSM), hinders an evidence-based response to the HIV epidemics. Respondent-driven sampling (RDS) was developed to overcome sampling challenges in studies of populations like MSM for which sampling frames are absent. Internet-based RDS (webRDS) can potentially circumvent limitations of the original RDS method. We aimed to implement and evaluate webRDS among a hidden population. Methods and Design: This cross-sectional study took place 18 February to 12 April, 2011 among MSM in Vietnam. Inclusion criteria were men, aged 18 and above, who had ever had sex with another man and were living in Vietnam. Participants were invited by an MSM friend, logged in, and answered a survey. Participants could recruit up to four MSM friends. We evaluated the system by its success in generating sustained recruitment and the degree to which the sample compositions stabilized with increasing sample size. Results: Twenty starting participants generated 676 participants over 24 recruitment waves. Analyses did not show evidence of bias due to ineligible participation. Estimated mean age was 22 year and 82% came from the two large metropolitan areas. 32 out of 63 provinces were represented. The median number of sexual partners during the last six months was two. The sample composition stabilized well for 16 out of 17 variables. Conclusion: Results indicate that webRDS could be implemented at a low cost among Internet-using MSM in Vietnam. WebRDS may be a promising method for sampling of Internet-using MSM and other hidden groups. Key words: Respondent-driven sampling, Online sampling, Men who have sex with men, Vietnam, Sexual risk behavio

Different facets of the raise and peel model

The raise and peel model is a one-dimensional stochastic model of a fluctuating interface with nonlocal interactions. This is an interesting physical model. It's phase diagram has a massive phase and a gapless phase with varying critical exponents. At the phase transition point, the model exhibits conformal invariance which is a space-time symmetry. Also at this point the model has several other facets which are the connections to associative algebras, two-dimensional fully packed loop models and combinatorics.Comment: 29 pages 17 figure

High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions

Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding

Recognition models to predict DNA-binding specificities of homeodomain proteins

Motivation: Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C2H2 zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes

Predicting the binding preference of transcription factors to individual DNA k-mers

Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible k-mers provide a new opportunity to study DNA–protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members. Results: We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF–DNA recognition, and suggest a rational approach for future analyses of TF families. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.Canadian Institutes of Health ResearchOntario Research FundNational Institutes of Health (U.S.)National Human Genome Research Institute (U.S.