Morphological evolution of edge-hillocks on single-crystal films having anisotropic drift-diffusion under the capillary and electromigration forces

The morphological evolution of hillocks at the unpassivated sidewalls of single-crystal metallic thin film interconnects is investigated via computer simulations using the free-moving boundary value problem. The effect of drift-diffusion anisotropy on the development of surface topographical scenarios is fully explored under the action of electromigration and capillary forces, utilizing numerous combinations of the surface texture, the drift-diffusion anisotropy and the direction of the applied electric field. The simulation studies yield analytical relationships for the velocity of the surface solitary waves and the drift velocity of electromigration-induced internal voids as a function of the applied current densities, which contain intrinsic and structural properties of the single-crystal thin films. The threshold value of the applied current density, above which electromigration-induced internal voids can be formed and may cause the catastrophic failure of interconnects by breaching, also appears explicitly in this relationship.     

Constructing Pseudo-Random Permutations with a Prescribed Structure

We show how to construct pseudo-random permutations that satisfy a certain cycle restriction, for example that the permutation be cyclic (consisting of one cycle containing all the elements) or an involution (a self-inverse permutation) with no fixed points. The construction can be based on any (unrestricted) pseudo-random permutation. The resulting permutations are defined succinctly and their evaluation at a given point is efficient. Furthermore, they enjoy a fast forward property, i.e. it is possible to iterate them at a very small cost. Received 10 August 2000 and revised 30 September 2000 Online publication 9 April 2001     

Collaborative detection and mitigation of DDoS in software-defined networks

The Journal of Supercomputing - This research presents the detection and mitigation of distributed denial of service (DDoS) in software defined networks (SDN). The proposed method consists of three...     

Randomized range-maxima in nearly-constant parallel time

Given an array ofn input numbers, therange-maxima problem is that of preprocessing the data so that queries of the type what is the maximum value in subarray [i..j] can be answered quickly using one processor. We present a randomized preprocessing algorithm that runs inO(log^* n) time with high probability, using an optimal number of processors on a CRCW PRAM; each query can be processed in constant time by one processor. We also present a randomized algorithm for a parallel comparison model. Using an optimal number of processors, the preprocessing algorithm runs inO( (n)) time with high probability; each query can be processed inO ( (n)) time by one processor. (As is standard, (n) is the inverse of Ackermann function.) A constant time query can be achieved by some slowdown in the performance of the preprocessing stage.     

High-Temperature Stiffness and Damping Measurements to Monitor the Glassy Intergranular Phase in Liquid-Phase-Sintered Silicon Carbides

Detailed stiffness and internal friction ( Q ⁻¹) versus temperature curves were obtained for liquid-phase-sintered silicon carbides using advanced resonant beam analysis up to 1400°C. As-sintered materials display a stable Q ⁻¹-peak near 1100°C, superimposed on an increasing background. The change of stiffness associated with the damping peak is quantitatively related to the amount of matter in pockets of the amorphous intergranular phase in which the refractory SiC matrix grains are embedded. The successful removal of the amorphous pockets by annealing at 1900°C is deduced from the disappearance of the damping peak and confirmed with transmission electron microscopy.     

Transfer learning in deep neural network-based receiver coil sensitivity map estimation

Magnetic Resonance Materials in Physics, Biology and Medicine - The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of...     

Micro-PIV measurements of induced-charge electro-osmosis around a metal rod

A cylindrical gold-coated stainless steel rod was positioned at the center of a straight microchannel connecting two fluid reservoirs on either end. The microchannel was filled with 1 mM KCl containing 0.5 μm diameter carboxylate-modified spherical particles. Induced-charge electro-osmotic (ICEO) flow occurred around the metallic rod under a sinusoidal AC electric field applied using two platinum electrodes. The ICEO flows around the metallic rod were measured using micro particle image velocimetry (micro-PIV) technique as functions of the AC electric field strength and frequency. The present study provides experimental data about ICEO flow in the weakly nonlinear limit of thin double layers, in which, the charging dynamics of the double layer cannot be presented analytically. The measured ICEO flow pattern qualitatively agrees with the theoretical results obtained by Squires and Bazant (J Fluid Mech 509:217–252, 2004). Flow around the rod is quadrupolar, driving liquid towards the rod along the electric field and forcing it away from the rod in the direction perpendicular to the imposed electric field. The measured ICEO flow velocity is proportional to the square of the electric field strength, and depends on the applied AC frequency.     

On Locally Decodable Codes, Self-Correctable Codes, and t-Private PIR

A k-query locally decodable code (LDC) allows to probabilistically decode any bit of an encoded message by probing only k bits of its corrupted encoding. A stronger and desirable property is that of self-correction, allowing to efficiently recover not only bits of the message but also arbitrary bits of its encoding. In contrast to the initial constructions of LDCs, the recent and most efficient constructions are not known to be self-correctable. The existence of self-correctable codes of comparable efficiency remains open.     

Selective determination of urea using urease immobilized on ZnO nanowires

Well-aligned zinc oxide (ZnO) nanowire arrays were fabricated on gold-coated plastic substrates using a low-temperature aqueous chemical growth (ACG) method. The ZnO nanowire arrays with 50–130 nm diameters and ∼1 μm in lengths were used in an enzyme-based urea sensor through immobilization of the enzyme urease that was found to be sensitive to urea concentrations from 0.1 mM to 100 mM. Two linear sensitivity regions were observed when the electrochemical responses (EMF) of the sensors were plotted vs. the logarithmic concentration range of urea from 0.1 mM to 100 mM. The proposed sensor showed a sensitivity of 52.8 mV/decade for 0.1–40 mM urea and a fast response time less than 4 s was achieved with good selectivity, reproducibility and negligible response to common interferents such as ascorbic acid and uric acid, glucose, K⁺ and Na⁺ ions.     

Modelling of residual stresses in the shot peened material C-1020 by artificial neural network

This study consists of two cases: (i) The experimental analysis: Shot peening is a method to improve the resistance of metal pieces to fatigue by creating regions of residual stress. In this study, the residual stresses induced in steel specimen type C-1020 by applying various strengths of shot peening, are investigated using the electrochemical layer removal method. The best result is obtained using 0.26 mm A peening strength and the stress encountered in the shot peened material is ?276 MPa, while the maximum residual stress obtained is ?363 MPa at a peening strength of 0.43 mm A. (ii) The mathematical modelling analysis: The use of ANN has been proposed to determine the residual stresses based on various strengths of shot peening using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with four neurons in the hidden layer, which made it possible to predict residual stress with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the R² values are 0.996112 and 0.99896 for annealed before peening and shot peened only, respectively. Similarly, these values for testing data are 0.995858 and 0.999143, respectively. As seen from the results of mathematical modelling, the calculated residual stresses are obviously within acceptable uncertainties.