F. Djeffal

Analytical analysis of nanoscale multiple gate MOSFETs including effects of hot-carrier induced interface charges

As the channel length rapidly shrinks down to the nanoscale regime, the multiple gate MOSFETs structures have been considered as potential candidates for a CMOS device scaling due to its good short-channel-effects (SCEs) immunity. Therefore, in this work we investigate the scaling capability of Double Gate (DG) and Gate All Around (GAA) MOSFETs using an analytical analysis of the two dimensional Poisson equation in which the hot-carrier induced interface charge effects have been considered. Basing on this analysis, we have found that the degradation becomes more important when the channel length gets shorter, and the minimum surface potential position is affected by the hot-carrier induced localized interface charge density. Using this analysis, we have studied the scaling limits of DG and GAA MOSFETs and compared their performances including the hot-carrier effects. Our obtained results showed that the analytical analysis is in close agreement with the 2-D numerical simulation over a wide range of devices parameters. The proposed analytical approach may provide a theoretical basis and physical insights for multiple gate MOSFETs design including the hot-carrier degradation effects.

Electrical Performance Optimization of Nanoscale Double-Gate MOSFETs Using Multiobjective Genetic Algorithms

In this paper, a new multiobjective genetic algorithm (MOGA)-based approach is proposed to optimize the electrical performance of double-gate (DG) MOSFETs for nanoscale CMOS digital applications. The proposed approach combines the universal optimization and fitting capability of MOGAs and the cost-effective optimization concept of quantum correction to achieve reliable and optimized designs of DG MOSFETs for nanoelectronics analog and digital circuit simulations. The dimensional and electrical parameters of the DG MOSFET (threshold voltage rolloff, off-current, drain-induced barrier lowering, subthreshold swing ( S), output conductance, and transconductance) have been ascertained, and a compact analytical expression, including quantum effects, has been presented. The developed compact models are used to formulate different objective functions, which are the prerequisite of the multiobjective optimization. The optimized design can also be incorporated into a circuit simulator to study and show the impact of our approach on a nanoscale CMOS-based circuit design.

Multi-objective genetic algorithms based approach to optimize the electrical performances of the gate stack double gate (GSDG) MOSFET

In this paper, a Multi-Objective Genetic Algorithm (MOGA)-based approach is proposed to study and optimize the electrical behavior of Gate Stack Double Gate (GSDG) MOSFET for deep submicron CMOS digital and analog circuit applications. The analytical models, which describe the electrical behavior, of the (GSDG) MOSFET such as OFF-current, threshold voltage roll-off, drain induced barrier lowering (DIBL), subthreshold swing and transconductance have been ascertained. The proposed compact models are used to formulate the objective functions, which are the pre-requisite of multi-objective genetic algorithms. The problem is then presented as a multi-objective optimization one where the subthreshold and saturation parameters are considered simultaneously. The proposed approach is used to find the optimal electrical and dimensional transistor parameters in order to obtain and explore the better transistor performances for analog and digital CMOS-based circuit applications.

Continuous analytic I?V model for GS DG MOSFETs including hot-carrier degradation effects

We have studied the influence of hot-carrier degradation effects on the drain current of a gate-stack double-gate (GS DG) MOSFET device. Our analysis is carried out by using an accurate continuous current?voltage (I?V) model, derived based on both Poissons and continuity equations without the need of charge-sheet approximation. The developed model offers the possibility to describe the entire range of different regions (subthreshold, linear and saturation) through a unique continuous expression. Therefore, the proposed approach can bring considerable enhancement at the level of multi-gate compact modeling including hot-carrier degradation effects.

Drain current model for undoped Gate Stack Double Gate (GSDG) MOSFETs including the hot-carrier degradation effects

In this paper, analytical models of drain current and small signal parameters for undoped symmetric Gate Stack Double Gate (GSDG) MOSFETs including the interfacial hot-carrier degradation effects are presented. The models are used to study the device behavior with the interfacial traps densities. The proposed model has been implemented in the SPICE circuit simulator and the capabilities of the model have been explored by circuit simulation example. The developed approaches are verified and validated by the good agreement found with the 2D numerical simulations for wide range of device parameters and bias conditions. GSDG MOSFET design and the accurate proposed model can alleviate the critical problem and further improve the immunity of hot-carrier effects of DG MOSFET-based circuits after hot-carrier damage.

Double-Gate Graphene Nanoribbon Field-Effect Transistor for DNA and Gas Sensing Applications: Simulation Study and Sensitivity Analysis

In this paper, new sensors based on a double-gate (DG) graphene nanoribbon field-effect transistor (GNRFET), for high-performance DNA and gas detection, are proposed through a simulation-based study. The proposed sensors are simulated by solving the Schrödinger equation using the mode space non-equilibrium Greens function formalism coupled self-consistently with a 2D Poisson equation under the ballistic limits. The dielectric and work function modulation techniques are used for the electrical detection of DNA and gas molecules, respectively. The behaviors of both the sensors have been investigated, and the impacts of variation in geometrical and electrical parameters on the sensitivity of sensors have also been studied. In comparison to other FET-based sensors, the proposed sensors provide not only higher sensitivity but also better electrical and scaling performances. The obtained results make the proposed DG-GNRFET-based sensors as promising candidates for ultra-sensitive, small-size, low-power and reliable CMOS-based DNA, and gas sensors.

Role of Optimized Grooves Surface-Textured Front Glass in Improving TiO 2 Thin-Film UV Photodetector Performance

In this paper, the impact of the surface-textured front glass on the absorption of TiO2/glass thin-film ultraviolet (UV) photodetector is investigated, in order to achieve the dual role of increasing the scattering of UV-light as well as reducing the refracting UV-light in the glass. The efficient control of these phenomena may lead to more electric field confinement and UV-light trapping in TiO2 absorber layer. Moreover, semianalytical modeling combined with particle swarm optimization is carried out for studying and enhancing the metal-semiconductor-metal photodetector optical and electrical performances. The results obtained from our semianalytical investigation are validated by comparison with the experimental data. It is found that the absorbance increases significantly by about 51% in optimized design over the planar structure, which is expected to improve the photodetector figures of merit. In this context, photodetector with optimized grooves texturization exhibits a 341% improvement, in terms of responsivity, in comparison with the planar structure and 275% improvement with respect to the textured device without optimization. The obtained results make the proposed design methodology a promising alternative for high-performance optoelectronic applications.

Neural classification method in fault detection and diagnosis for voltage source inverter in variable speed drive with induction motor

These days, electrical drives generally associate inverter and induction machine. Thus, these two elements must be taken into account in order to provide a relevant diagnosis of these electrical systems. The aim of this paper is to study the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor. The proposed approach is a neural network classification applied to the fault diagnosis of a field oriented drive of induction motor. Multilayer perception (MLP) networks are used to identify the type and location of occurring fault using the stator Concordia mean current vector. In the case of a single fault occurrence, a localization domain made with seven patterns is built. With the possibility of occurrence of two faults simultaneously, there are twenty-two different patterns. Simulated experimental results on 1.5-kW induction motor drives show the effectiveness of the proposed approach with a classification performance over than 95%.

Wavelet-based ECG signals compression using SPIHT technique and VKTP coder

In this paper, an efficient Electrocardiogram (ECG) signal compression method based on wavelet transform is presented. The proposed method combines the adapted SPIHT (Set Partitioning In Hierarchical Trees) method with VKTP (Vector K-Tree Partitioning) coder. The SPIHT method is based on the use of wavelet transform which is very well suited to locate the energy of the signal in fewer coefficients. Using the VKTP algorithm, to encode the generated bit stream of SPIHT algorithm, we achieve high compression performances. The tests of this lossy compression/ decompression technique are performed on many ECG records from Arrhythmia Database. The obtained results illustrate the capabilities of the proposed approach to improve the compression ratio while maintaining a good signal quality.

Multigate RADFET Dosimeter for Radioactive Environment Monitoring Applications

In this chapter, a new radiation sensitive FET (RADFET) dosimeter design (called the Dual-Dielectric Gate All Around DDGAA RADFET dosimeter) to improve the radiation sensitivity performance and its analytical analysis have been proposed, investigated and expected to improve the sensitivity behavior and fabrication process for RADFET dosimeter-based applications. Analytical models have been developed to predict and compare the performance of the proposed design and conventional (bulk) RADFET, where the comparison of device architectures shows that the proposed design exhibits a superior performance with respect to the conventional RADFET in term of fabrication process and sensitivity performances. The proposed design has linear radiation sensitivities of approximately \(95.45\,\upmu \mathrm{{V/Gy}}\) for wide irradiation dose range (from \(\mathrm{{Dose}}=50\,\mathrm{{Gy}}\) to \(\mathrm{{Dose}}=3000\,\mathrm{{Gy}}\)). Our results showed that the analytical analysis is in close agreement with the 2-D numerical simulation over a wide range of devices parameters. The proposed device and the Artificial Neural Networks (ANNs) have been used to study and show the impact of the proposed dosimeter on the environment monitoring and remote sensing applications. The obtained results make the DDGAA RADFET dosimeter a promising candidate for environment monitoring applications.