Improvement in the voltage grading (axial and radial) of the generator column of a Van de Graaf generator by the use of a resistor chain

It is well known that the use of a series of resistors, connected between the equipotential rings of a Van de Graaff generator, improves the axial voltage grading of the generator. The work reported in this paper shows how the resistor chain also improves the radial voltage gradient. The electrolytic field mapping technique was adopted in the present work.

RF Sputtered Er2O3 Thin Films as High-k Gate Dielectrics for Germanium MOS Devices

We report the material and electrical properties of Erbium Oxide (Er2O3) thin films grown on n-Ge (100) by RF sputtering. The properties of the films are correlated with the processing conditions. The structural characterization reveals that the films annealed at 550 degrees C, has densified as compared to the as-grown ones. Fixed oxide charges and interface charges, both of the order of 10(13)/cm(2) is observed.

A New Threshold Voltage Model for Omega Gate Cylindrical Nanowire Transistor

In this work, for the first time, we present a physically based analytical threshold voltage model for omega gate silicon nanowire transistor. This model is developed for long channel cylindrical body structure. The potential distribution at each and every point of the of the wire is derived with a closed form solution of two dimensional Poisson's equation, which is then used to model the threshold voltage. Proposed model can be treated as a generalized model, which is valid for both surround gate and semi-surround gate cylindrical transistors. The accuracy of proposed model is verified for different device geometry against the results obtained from three dimensional numerical device simulators and close agreement is observed.

Rotorcraft parameter estimation using radial basis function neural network

Increased emphasis on rotorcraft performance and perational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. In the present work, the rotorcraft aerodynamic parameters are computed using radial basis function neural networks (RBFN) in the presence of both state and measurement noise. The effect of presence of outliers in the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data. (C) 2010 Elsevier Inc. All rights reserved.

A Computationally Efficient Generalized Poisson Solution for Independent Double-Gate Transistors

Previous techniques used for solving the 1-D Poisson equation ( PE) rigorously for long-channel asymmetric and independent double-gate (IDG) transistors result in potential models that involve multiple intercoupled implicit equations. As these equations need to be solved self-consistently, such potential models are clearly inefficient for compact modeling. This paper reports a different rigorous technique for solving the same PE by which one can obtain the potential profile of a generalized IDG transistor that involves a single implicit equation. The proposed Poisson solution is shown to be computationally more efficient for circuit simulation than the previous solutions.

On the investigation of singular stress field around radial cracks in annulii subjected to diametrical tension

Results of photoelastic investigation conducted on annulii containing a radial crack at inner edge and subjected to diametrical tension are reported. The cracks are oriented at 90°, 60° and 45° to the loading direction. The Stress-Intensity Factors (SIFs) were determined by analysing the crack-tip stress fields. Smith and Smith's method [Engng Fracture Mech.4, 357–366 (1972)] and a modified method developed earlier by the authors (to be published) were adopted in the evaluation of SIFs.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Steady compressible flow of granular materials through a wedge-shaped hopper: The smooth wall, radial gravity problem

A continuum model based on the critical state theory of soil mechanics is used to generate stress and density profiles, and to compute discharge velocities for the plane flow of cohesionless materials. Two types of yield loci are employed, namely, a yield locus with a corner, and a smooth yield locus. The yield locus with a corner leads to computational difficulties. For the smooth yield locus, results are found to be relatively insensitive to the shape of the yield locus, the location of the upper traction-free surface and the density specified on this surface. This insensitivity arises from the existence of asymptotic stress and density fields, to which the solution tends to converge on moving down the hopper. Numerical and approximate analytical solutions are obtained for these fields and the latter is used to derive an expression for the discharge velocity. This relation predicts discharge velocities to within 13% of the exact (numerical) values. While the assumption of incompressibility has been frequently used in the literature, it is shown here that in some cases, this leads to discharge velocities which are significantly higher than those obtained by the incorporation of density variation.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

A Compact Model incorporating Quantum Effects for Ultra-Thin-Body Double-Gate MOSFETs

We propose a compact model which predicts the channel charge density and the drain current which match quite closely with the numerical solution obtained from the Full-Band structure approach. We show that, with this compact model, the channel charge density can be predicted by taking the capacitance based on the physical oxide thickness, as opposed to C-eff, which needs to be taken when using the classical solution.

Effect of a linear potential variation on the gate of an FET

This correspondence aims at reporting the results of an analysis carried out to find the effect of a linear potential variation on the gate of an FET.

Steady incompressible flow of cohesionless granular materials through a wedge-shaped hopper: Frictional 14kinetic solution to the smooth wall, radial gravity problem

Hybrid frictional-kinetic equations are used to predict the velocity, grain temperature, and stress fields in hoppers. A suitable choice of dimensionless variables permits the pseudo-thermal energy balance to be decoupled from the momentum balance. These balances contain a small parameter, which is analogous to a reciprocal Reynolds number. Hence an approximate semi-analytical solution is constructed using perturbation methods. The energy balance is solved using the method of matched asymptotic expansions. The effect of heat conduction is confined to a very thin boundary layer near the exit, where it causes a marginal change in the temperature. Outside this layer, the temperature T increases rapidly as the radial coordinate r decreases. In particular, the conduction-free energy balance yields an asymptotic solution, valid for small values of r, of the form T proportional r-4. There is a corresponding increase in the kinetic stresses, which attain their maximum values at the hopper exit. The momentum balance is solved by a regular perturbation method. The contribution of the kinetic stresses is important only in a small region near the exit, where the frictional stresses tend to zero. Therefore, the discharge rate is only about 2.3% lower than the frictional value, for typical parameter values. As in the frictional case, the discharge rate for deep hoppers is found to be independent of the head of material.

Analytical modeling of quantum threshold voltage for triple gate MOSFET

In this work a physically based analytical quantum threshold voltage model for the triple gate long channel metal oxide semiconductor field effect transistor is developed The proposed model is based on the analytical solution of two-dimensional Poisson and two-dimensional Schrodinger equation Proposed model is extended for short channel devices by including semi-empirical correction The impact of effective mass variation with film thicknesses is also discussed using the proposed model All models are fully validated against the professional numerical device simulator for a wide range of device geometries (C) 2010 Elsevier Ltd All rights reserved