Dielectric-breakdown-induced epitaxy: a universal breakdown defect in ultrathin gate dielectrics

The breakdown phenomena in SiO/sub x/N/sub y/ (EOT=20 /spl Aring/) gate dielectric under a two- stage constant voltage stress in inversion mode are physically analyzed with the aid of transmission electron microscopy. The results show that dielectric-breakdown-induced epitaxy (DBIE) remains as one of the major failure defects responsible for gate dielectric breakdown evolution even for a stress voltage as low as 2.5 V. Based on the results, the same failure mechanism i.e., presence of DBIE would be responsible for the degradation in ultrathin gate dielectrics for gate voltage below 2.5 V. It is believed that DBIE will be present in MOSFETs failed at nominal operating voltage.     

Large-Eddy Simulation of High Reynolds Number Turbulent Flow Past a Square Cylinder

Flow past a square cylinder at a Reynolds number of 21,400 has been studied numerically using the large-eddy simulation technique. A dynamic subgrid-scale stress model has been used for the small scales of turbulence. The time- and span-averaged axial and transverse velocities in the downstream of the cylinder are in good agreement with the experimental results. The distribution of turbulent normal and shear stresses is also well predicted. The coherent and incoherent components of turbulent fluctuations at some specified phases have been separated and their relative magnitudes downstream of the cylinder have been compared. The comparison shows more coherence in the near wake than the far wake, while the coherent and incoherent components are of comparable magnitude in the far wake. The far wake shows irregular phase-averaged structures.     

Collision-free spatial hash functions for structural analysis of billion-vertex chemical bond networks

State-of-the-art molecular dynamics (MD) simulations generate massive datasets involving billion-vertex chemical bond networks, which makes data mining based on graph algorithms such as K-ring analysis a challenge. This paper proposes an algorithm to improve the efficiency of ring analysis of large graphs, exploiting properties of K-rings and spatial correlations of vertices in the graph. The algorithm uses dual-tree expansion (DTE) and spatial hash-function tagging (SHAFT) to optimize computation and memory access. Numerical tests show nearly perfect linear scaling of the algorithm. Also a parallel implementation of the DTE + SHAFT algorithm achieves high scalability. The algorithm has been successfully employed to analyze large MD simulations involving up to 500 million atoms.     

Small torsional vibration superposed on finitely deformed state of a circular cylindrical rod of transversely isotropic elastic material

Starting with a class of small deformations superposed on a finitely deformed state of a transversely isotropic elastic solid, we study a problem of small torsional vibration superposed on homogeneous finitely deformed state of a circular cylindrical rod made of transversely isotropic elastic material. It has been found that free vibration is possible and, due to anisotropy, the speed of propagation of waves of torsion along the cylinder is increased or decreased according as the initial stressed state is under tension or compression.     

Chemical,rheological and parotta making characteristics of flourmill streams

Chemical, rheological and parotta-making characteristics of wheat-milling flour streams were studied with the main aim of developing a speciality flour for parotta. Important quality characteristics such as ash, flour colour, dry gluten content, sedimentation value, free and bound lipid content, farinograph and alveograph characteristics of the mill streams were determined. The studies showed an increase in ash, gluten content and sedimentation value with increasing numbers of breaks in the flour streams. An increase in percentage whiteness and bound lipids was observed in the first five reduction passages (C1 to C5). The alveograph characteristics indicated that the average abscissa at rupture (L) length of the curve increased with increasing reduction streams from C1 to C5, and also that the curves were better balanced when compared to 1 BK to 5 BK. The parottas made from the first five break passages had a decreased spread ratio, dull brown colour, and fused layers and lower overall quality score (43–79). The initial reduction streams (C1 to C5) produced good quality parottas in terms of appearance, spread, layers and texture. The shear force values indicating the texture of the product ranged from 1,120 to 1,250 g. The speciality flour made by combining C1 to C5 streams had 0.45% ash, 9.63% dry gluten, 45 ml sodium dodecyl sulphate sedimentation value, 0.7% free lipid and 1.8% bound lipid and produced parottas having creamish white colour, excellent pliability, thin, transparent and distinct layers, soft texture and moderate chewiness. In the mouth the parottas broke down easily, without leaving any residue. The overall quality score of parotta was highest (93.5) for the speciality flour when compared to break and reduction streams (43–77 and 56–92 respectively).     

Accuracy of self-reported data for estimating crash severity

Estimated traveling speed and speed limit have typically been used in population-based surveillance data to estimate crash severity. The accuracy of these measures in predicting crash severity is unknown. The Partners for Child Passenger Safety (PCPS) surveillance system offers a unique opportunity to compare these measures, as well as a novel measure of crash severity, “self-report” delta-V, to the accepted measure of delta-V estimated during detailed crash-investigations in 118 crashes. This “self-report” delta-V was computed from the estimated traveling speeds and direction of impact obtained from telephone interviews with drivers. These “self-reported” delta-V estimates are modestly associated with crash-investigation delta-V estimates, with the degree of association a function of the direction of impact: when the respondent was struck from the rear, the degree of association is strong; frontal, side, and single-vehicle crashes yield weaker associations. This “self-reported” delta-V measure, however, is a substantial improvement over use of estimated traveling speed or speed limit only.     

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.     

Multifrequency ultrasound transducers for conformal interstitial thermal therapy

Control over the pattern of thermal damage generated by interstitial ultrasound heating applicators can be enhanced by changing the ultrasound frequency during heating. The ability to change transmission frequency from a single transducer through the use of high impedance front layers was investigated in this study. The transmission spectrum of multifrequency transducers was calculated using the KLM equivalent circuit model and verified with experimental measurements on prototype transducers. The addition of a quarter-wavelength thick PZT (unpoled) front layer enabled the transmission of ultrasound at two discrete frequencies, 4.7 and 9.7 MHz, from a transducer with an original resonant frequency of 8.4 MHz. Three frequency transmission at 3.3, 8.4, and 10.8 MHz was possible for a transducer with a half-wavelength thick front layer. Calculations of the predicted thermal lesion size at each transmission frequency indicated that the depth of thermal lesion could be varied by a factor of 1.6 for the quarter-wavelength front layer. Heating experiments performed in excised liver tissue with a dual-frequency applicator confirmed this ability to control the shape of thermal lesions during heating to generate a desired geometry. Practical interstitial Designs that enable the generation of shaped thermal lesions are feasible.