Dissipative based adaptive reliable sampled-data control of time-varying delay systems

This paper is concerned with the problem of dissipative based adaptive reliable controller for a class of time delay systems subject to actuator failures and time-varying sampling with a known upper bound on the sampling intervals. By constructing a proper Lyapunov-Krasovskii functional which fully uses the available information about the actual sampling pattern and time delays, a new set of sufficient conditions is derived to obtain the required result. Then, a dissipative based adaptive sampled-data controller is designed such that the resulting closed-loop system is reliable in the sense that it is asymptotically stable and has the prescribed dissipative performance under given constraints. The existence condition of the desired dissipative based adaptive reliable sampled-data controller is obtained in terms of linear matrix inequalities. Further, the performance of the proposed controller is implemented on a liquid propellant rocket motor with a pressure feeding system model. The simulation results show the effectiveness and better performance of the proposed adaptive reliable sampled-data controller over conventional reliable controller.     

Impact of temperature on RF characteristics and electrical noise analysis of an L‐shaped gate tunnel FET with hetero‐stacked source configuration

In this article, a pocket doped hetero‐stacked L‐shaped gate silicon‐on‐insulator (SOI) tunnel FET (HS‐LG‐TFET) has been proposed and investigated. The band‐to‐band tunneling (BTBT) feature of LG‐TFET in a direction perpendicular to the channel facilitates higher ON current due to relatively larger tunneling area. The channel appears to be U‐shaped that is primarily distributed along the vertical direction, which increases the device scalability. Besides, the HS source architecture owns an upper source layer, which consists of larger bandgap material silicon and an underlying source layer, which consists of smaller bandgap material germanium. This underlying layer of smaller bandgap material in HS‐LG‐TFET provides enhanced ON current as well as steeper subthreshold swing behavior. The electrical noise behavior of the proposed structure is addressed to test its viability. Furthermore, a complete radio frequency (RF) characterization, including transconductance, capacitances, cutoff frequency, gain bandwidth product, maximum oscillation frequency, transit time, and minimum noise figure of the proposed device are analyzed to examine its analog applicability. Moreover, for checking the reliability issues associated with temperature, the temperature dependence on transfer and RF characteristics are also explored and presented. Further, the non‐quasi‐static equivalent circuit of the proposed structure is presented to analyze its behavior in the high frequency range.     

Effect of Ge Mole Fraction on Electrical Parameters of Si1−xGex Source Step-FinFET and its Application as an Inverter

Silicon - This paper proposes device geometry of Fin-Field-Effect-Transistor (FinFET) with a step-fin. The source region of the proposed device consists of Si1−xGexand the effects of Ge-mole...     

Subthreshold surface potential and drain current models for short-channel pocket-implanted MOSFETs

An analytical subthreshold surface potential model for short-channel pocket-implanted (double-halo) MOSFET is presented. The effect of the depletion layers around the source and drain junctions on channel depletion layer depth, which is very important for short-channel devices, is included. Using this surface potential, a drift-diffusion based analytical subthreshold drain current model for short-channel pocket-implanted MOSFETs is also proposed. A physically-based empirical modification of the channel conduction layer thickness that was originally proposed for relatively long-channel conventional device is made for such short-channel double-halo devices. Very good agreement for both the surface potential and drain current is observed between the model calculation and the prediction made by the 2-D numerical device simulation using Dessis.     

Effect of viscous dissipation on finding dual solutions for mixed convection boundary layer flow for nanofluid

The effect of viscous dissipation on mixed convection boundary layer flow for Ag‐water nanofluid under steady‐state condition has been studied numerically for both the buoyancy assisting and opposing flows over a vertical semi‐infinite flat plate. A new co‐ordinate system has been introduced to transform the governing partial differential equations (PDEs) to facilitate the numerical calculations. Then, the local similarity method has been used for approximating the transformed PDEs to ordinary differential equations. Further, the quasi‐linearization method has been introduced to linearize the nonlinear equations and then numerical integration has been carried out using implicit trapezoidal rule along with the principle of superposition. For higher Pr, the coupled differential equations behave like stiff differential equations. To overcome the situation, orthonormalization process has been introduced. The effects of solid volume fraction of nanoparticles , the mixed convection parameter , Prandtl number , and Eckart number have been analyzed on the heat transfer and flow characteristics. It has been observed that the dual solutions are obtained for buoyancy opposing flow only and the range of dual solutions have become wider with the increases in . Further, nanofluids enhance the heat transfer process as compared to conventional heat transfer fluids . Moreover, the addition of viscous dissipation causes less heat transfer in the boundary.     

Ramp‐Reversal Memory and Phase‐Boundary Scarring in Transition Metal Oxides

Transition metal oxides are complex electronic systems that exhibit a multitude of collective phenomena. Two archetypal examples are VO₂ and NdNiO₃, which undergo a metal–insulator phase transition (MIT), the origin of which is still under debate. Here this study reports the discovery of a memory effect in both systems, manifested through an increase of resistance at a specific temperature, which is set by reversing the temperature ramp from heating to cooling during the MIT. The characteristics of this ramp‐reversal memory effect do not coincide with any previously reported history or memory effects in manganites, electron‐glass or magnetic systems. From a broad range of experimental features, supported by theoretical modelling, it is found that the main ingredients for the effect to arise are the spatial phase separation of metallic and insulating regions during the MIT and the coupling of lattice strain to the local transition temperature of the phase transition. We conclude that the emergent memory effect originates from phase boundaries at the reversal temperature leaving “scars” in the underlying lattice structure, giving rise to a local increase in the transition temperature. The universality and robustness of the effect shed new light on the MIT in complex oxides.     

Impact of process variation on the RF and stability performance of SiGe source-based epitaxial layer TFET

Journal of Computational Electronics - In this study, the RF and stability aspects of SiGe source-based epitaxial layer tunnel field-effect transistor (SiGe source ETLTFET) are explored by...     

Effect of Coiling Temperature on the Microstructure and Mechanical Properties of Hot-Rolled Ti–Nb Microalloyed Ultra High Strength Steel

A novel low carbon Ti–Nb microalloyed hot rolled steel with minimum yield strength of 700 MPa and good balance of stretch-flangeability and impact toughness has been developed by controlled thermo-mechanical processing following thin slab direct rolling route. In the present work, the effects of two coiling temperatures on the resulting microstructure, micro-texture and mechanical properties on this Ti–Nb microalloyed steel have been studied. It is observed that increase in coiling temperature from 520 to 580 °C significantly affects the mechanical properties. Higher dislocation density and increased precipitation along with slightly smaller grain size is observed for 580 °C coiling temperature resulting in about 50 MPa increase in yield and tensile strengths as compared to 520 °C coiling temperature.     

A neurocomputing model for SODAR structure classification

The manual identification of different types of atmospheric microstructures recorded by SODAR (SOund Detection And Ranging) is a tedious task and can be performed only by an expert with broad experience. To avoid this manual task, a neural network based method of SODAR structure classification system is proposed. This method is developed based on past observations of various meteorological parameters such as temperature, relative humidity and vapour pressure, along with different features computed from the SODAR structure data, which are images representing the dynamics of the planetary boundary layer (PBL). The patterns of these images indicate the structure of different thermal patterns of the atmosphere. We propose a neural network model whose architecture combines multilayer perceptron networks (MLPs) to realize better performance after capturing the seasonality and other related effects in the atmospheric data. We also demonstrate that the use of appropriate features can further improve performance of the prediction system. These observations inspired us to use a feature selection neural network which can select good features online while learning the prediction task. The feature selection neural network is used as a preprocessor to select good features. The combined use of feature selection neural network and MLP, i.e. FSMLP (feature selection multilayer perception) results in a neural network system that uses only very few inputs but can produce a good classifier. Here we develop a real-time system that classifies the SODAR patterns automatically.