Electronic Nose, as an artificial olfaction system, has potential applications in environmental monitoring because of its proven ability to recognize and discriminate between a variety of different gases and odors. In this paper, we used a chemical sensor array to develop an electronic nose to detect and identify seven different gases (H₂, C₂H₂, CH₄, CH₃OCH₃, CO, NO₂, and NH₃). These gas sensors are chosen because of its hierarchical/doped nanostructure characteristics, which give them a very high sensitivity and low response time; we improve the linearity response and temperature dependence using models based on artificial neural networks. We used in Electronic nose a pattern recognition based on artificial neural network, which discriminates qualitatively and quantitatively seven gases and has a fast response.

In the last few years, an accelerated trend towards the miniaturization of nanoscale circuits has been recorded. In this context, the Tunneling Field-Effect Transistors (TFETs) are gaining attention because of their good subthreshold characteristics, high scalability and low leakage current. However, they suffer from low values of the ON-state current and severe ambipolar transport mechanism. The aim of this work is to investigate the performance of SiGe nanoscale Double Gate TFET device including low doped drain region. The electrical performance of the considered device is investigated numerically using ATLAS 2D simulator, where both scaling and reliability aspects of the proposed design are reported. In this context, we address the impact of the channel length, traps density and drain doping parameters on the variation of some figures of merit of the device namely the swing factor and the I_ON/I_OFF ratio. The obtained results indicate the superior immunity of the proposed design against traps induced degradation in comparison to the conventional TFET structure. Therefore, this work can offer more insights regarding the benefit of adopting channel materials and drain doping engineering techniques for future reliable low-power nanoscale electronic applications.

The estimation of the junction temperature (Tj) is very important factor for improving the reliability and efficiency of the power electronic converters. A new electro-thermal (ET) model of low voltage power MOSFET is described in this paper. The electro-thermal model allows fast estimation of the junction temperature, based on the transient thermal impedances (Zth) using the (RC) Foster thermal network model and total power losses. The parameters of the (RC) Foster thermal network model are extracted from the data provided by the manufacturer’s datasheet using particle swarm optimization (PSO) method. Moreover, a dc/dc Buck converter is also analyzed by simulation to evaluate the electro-thermal model. The simulation results indicate a good agreement between the proposed model and manufacturer’s data. Finally, the electro-thermal (ET) model simulation using this (RC) Foster thermal network model shows a reasonable accuracy for estimating the junction temperature in a Dc/Dc buck converter.

This paper presents a hybrid strategy combining compact analytical models of short channel Gate-All-Around Junctionless (GAAJ) MOSFET and metaheuristic-based approach for parameters optimization. The proposed GAAJ MOSFET design includes highly extension regions doping. The aim is to investigate the impact of this design on the RF and analog performances systematically and to show the immunity behavior against the short channel effects (SCEs) degradation. In this context, an analytical model via the meticulous solution of 2D Poisson equation, incorporating source/drain (S/D) extensions effect, has been developed and verified by comparing it with TCAD simulation results. A comparative evaluation between the proposed GAAJ MOSFET structure and the classical device in terms of RF/Analog performances is also investigated. The proposed design provides RF/Analog performances improvement. Furthermore, based on the presented analytical models, Genetic Algorithms (GA) optimization approach is used to optimize the design of S/D parameters. The optimized structure exhibits better performances, i.e., cut-off frequency and drive current are improved. Besides, it shows superior immunity behavior against the RF/Analog degradation due to the unwanted SCEs. The insights offered by the proposed paradigm will help to enlighten designer in future challenges facing the GAAJ MOSFET technology for high RF/analog applications.

The selectivity is one of the main challenges to develop a gas sensor, the good chemical species detection in a gaseous mixture decreasing the missed detections. The present paper proposes a new solution for gas sensor selectivity based on artificial neural networks (ANNs) and fuzzy logic (FL) algorithm. We first use ANNs to develop a gas sensor model in order to accurately express its behavior. In a second step, the FL and Matlab environment are used to create a database for a selective model, where the response of this one only depends on one chemical species. Analytical models for the gas sensor and its selective model are implemented into a Performance Simulation Program with Integrated Circuit Emphasis (PSPICE) simulator as an electrical circuit in order to prove the similarity of the analytical model output with that of the MQ-9 gas sensor where the output of the selective model only depends on one gas. Our results indicate the capability of the ANN-FL hybrid modeling for an accurate sensing analysis.

This paper proposes a new method based on a genetic algorithm (GA) approach to optimize the electrical parameters such as height barrier, ideality factor, fill factor, open-circuit voltage and power conversion efficiency, in order to improve the electrical performance of Schottky solar cells in an over wide range of temperature. Thus the parameters research process called objective function is used to find the optimal electrical parameters providing greater conversion efficiency. The proposed model results are also compared to experimental and analytical I-V data, where a good agreement has been found between them. Therefore, this approach may provide a theoretical basis and physical insights for Schottky solar cells.

In this paper, we study the postural behaviour of two categories of people: Post-CVA subjects suffering from cerebrovascular accident syndromes and healthy individuals under several levels of anterior–posterior and medial–lateral sinusoidal disturbances (0.1–0.5 Hz). These perturbations were produced from an omnidirectional platform called Isiskate. Afterwards, we have quantified seventy postural parameters, they were combined of linear stabilometric parameters and non-linear time dependent stochastic parameters using stabilogram diffusion analysis and some spectral attributes using power spectral density. The aim of our analysis is to reduce data dimensionality using principal component analysis (PCA). Furthermore, we proposed a new PCA-related criterion named: criterion of contribution in order to evaluate the contribution of every variable in the resulted system structure, and thus to eliminate the redundant postural characteristics. Afterwards, we highlighted some interesting distinctive parameters. The selected parameters were used thereafter in comparison between the studied groups. Finally, we created a classification model using support vector machines to distinguish stroke patients. Our proposed techniques help in understanding the human postural dynamics and facilitate the diagnosis of pathologies related to equilibrium which can be used to improve the rehabilitation services.

In this paper, different techniques of analysis have been used to study the effects of perturbations generated from a robotic mobile platform called Isiskate. These disturbances were applied on two categories of people: post-CVA subjects suffering from cerebrovascular accident and healthy individuals. Our aim is to analyze some assessment tools to distinguish between different postural behaviors. In relevant works, very few studies have addressed the use of nonlinear time-series methods in diagnosis of post-CVA pathological postural behavior. Furthermore, our tools are based on parametric and non-parametric identification procedures, that can yield to an insight on how to improve the examination time. As part of our analysis, the tests were established with several levels of sinusoidal vibrations, along the anterior–posterior (A/P) and medial–lateral (M/L) planes. The mobile platform allowed us to record a set of coordinates that includes center of pressure (COP) as a function of time. First, we have quantified some linear parameters and spectral characteristics using power spectral density (PSD). Thereafter, we have deduced stochastic parameters using stabilogram diffusion analysis (SDA), which revealed some interesting invariants. Then transfer functions between the platform velocity and COP trajectory were evaluated. They were carried out at frequencies from 0.1 Hz to 3.3 Hz. Furthermore, we accomplished a comparison of models based on both parametric and nonparametric identification methods. The combination of the proposed techniques has provided us an understanding of human control process by establishing a behavior model and helped us to distinguish patients with postural disorders. This improves postural analysis and facilitates the diagnosis of pathologies related to equilibrium which serves in rehabilitation.

This paper presents a new nanoforce sensor based on a suspended carbon nanotube gate field-effect transistor. To do so, a numerical investigation of Suspended Gate Silicon-on-Insulator MOSFET (SG-SOIMOSFET) is carried out using ATLAS 2D simulator. Based on the relationship between the nanotube’s deflection and the applied force, a comprehensive study of the proposed nanoforce sensor behavior is performed. Moreover, we describe the evolution of the drain current characteristics as a function of the applied force while examining the influence of capacity variation of the insulating gate on the drain current in the saturation region. It is found that the sensor has a good sensitivity of 230.68 ln(A)/pN. Our second contribution in this paper is to develop a model based on artificial neural networks (ANNs). We successfully integrate our neural model of nanoforce sensor as a new component in the ORCAD-PSPICE electric simulator library; this component must accurately express the behavior of the sensor. A second model based on neural networks, which deals with correction and linearization of the sensor output signal, is designed and implemented into the same simulator. The proposed device can be considered as a potential alternative for CMOS-based nanoforce sensing.

Gate engineering and highly doped source/drain region have been investigated to design a new DNA sensor for use in biomedical applications based on a double gate (DG) dielectric modulated (DM) junctionless (JL) metal oxide semiconductor field effect transistor (MOSFET) with triple material (TM) gate. Based on the dielectric modulation effect, DNA molecules in the nanogap cavity change due to the charge density of biomolecules, producing a change in the threshold voltage of the device. Analytical and numerical analysis was carried out to reveal the impact of physical parameters on the sensitivity of the proposed biosensor. Various characteristics, such as the surface potential, threshold voltage, and drain current were also investigated. The effectiveness of the proposed TM-DG-DM-JL-MOSFET structure with highly doped source/drain extensions is confirmed by comparison of the results with those for a conventional single-materiel (SM) gate DM-JL-MOSFET, revealing a good improvement in sensitivity and making the proposed structure an attractive solution for use in DNA-based sensor applications.

In this paper, a new Graphene nanoribbon (GNR) based Ge-phototransistor is proposed and investigated numerically by self-consistently solving the Schrödinger equation and Poisson equation using non-equilibrium Green's function (NEGF) formalism. An overall performance metrics comparison between both the conventional Si-based phototransistor and the proposed design is performed. It is found that the proposed GNR Ge-phototransistor provides better electrical and optical performances compared to the conventional counterpart. Moreover, using GNR material as a channel can improve the device performance not only enables a high I_on/I_off ratio, but also allows achieving a superior sensitivity for ultra-low optical powers. It is also revealed that the responsivity of the investigated design can be increased by reducing the GNR channel length. This underlines the outstanding capability of the proposed design for bridging the gap between modern nanoelectronic and nanophotonic technologies. In addition, the proposed GNR-based Ge-phototransistor can achieve an acceptable detectivity for very weak optical power intensities, in the order of some Femto-Watts, which leads to reduce the total power consumption associated with optical links. Therefore, the proposed GNR phototransistor pinpoints a new path toward achieving an ultrasensitive photoreceiver with low power consumption, which makes it potential alternative for chip-level Infrared communication and nano-optoelectronic applications.

In this paper, a new particle swarm optimization‐based approach is proposed for the geometrical optimization of the nanowires solar cells to achieve improved optical performance. The proposed hybrid approach combines the 3‐D numerical analysis using accurate solutions of Maxwell's equations and metaheuristic investigation to boost the solar cell total absorbance efficiency. Our purpose resides on modulating the electric field and increasing the light trapping capability by optimizing the radial solar cell geometrical parameters. Moreover, a comprehensive study of vertical core‐shell nanowire arrays optical parameters such as the integral absorption, reflection, and total absorbance efficiency is carried out, in order to reveal the optimized radial solar cells optical performance for low‐cost photovoltaic applications. We find that the proposed hybrid approach plays a crucial role in improving the nanowires solar cells optical performance, where the optimized design exhibits superior total absorbance efficiency and lower total reflection in comparison with those provided by the conventional planar design. The obtained results make the proposed global optimization approach valuable for providing high‐efficiency nanowires solar cells.

The performances of two full differential operational amplifiers (Op-Amps) telescopic and folded-cascode are evaluated to satisfy the stringent requirements on the amplifier to be used in a Multiplying Digital-to-Analog Converter (MDAC) stage of a pipelined ADC (Analog-to-Digital Converter). The paper shows the solutions found to reach high gain, wide bandwidth and short settling time without degrading too much the output swing. The Op-Amp specifications are extracted according to the ADC requirements, then the two Op-Amp topologies are designed, tested and their performances are compared. Simulation results show that the Op-Amp folded-cascode topology is more suitable architecture for pipelined ADC than the telescopic one. Moreover, the use of this type of Op-Amp generates an Integral Non-Linearity (INL) error less than that of the telescopic one. The analyses and simulation results are obtained using 0.18 µm AMS (Austria Mikro System) CMOS process parameters with a power supply voltage of 1.8V. The predicted performance is verified by analysis and simulations using Cadence EDA simulator.

An efficient small size electromagnetic energy harvesting sensor for low-DC-power applications is proposed. The sensor consists of two main parts: a dual polarisation square patch antenna used to collect the RF energy at a central frequency of 2.45 GHz, and two voltage doublers rectifier circuit for the RF-to-DC conversion. The overall size of the design is 50 × 50 × 6.2 mm ³ . Firstly, the antenna is designed using high-frequency structure simulator software; followed by the design of the rectifier circuit in advanced design system. After simulations, a sensor prototype is fabricated using F4B as the antenna substrate. Measurements show that the sensor achieves a comparatively high maximum measured efficiency of 41% for a power level of -10 dBm. The sensor has a simple structure, it is compact sized, light weight, and presents a high RF-to-DC conversion efficiency for low-RF-power levels which can be used to charge different low-DC-power devices.

An 8-bit 40-MS/s low power Multiplying Digital-toAnalog Converter (MDAC) for a pipelined-to-Analog to Digital converter (ADC) is presented.The conventional dedicated operational amplifier (Op-Amp) isperformed by using telescopic architecture that features low power and less-area. Further reduction of power and area is achieved by using multifunction 1.5bit/stage MDAC architecture. The design of the Op-Amp is performed by the elaboration of a program based on multiobjective genetic algorithms to allow automated optimization. The proposed program is used tofind the optimal transistors sizes (length and width) in order to obtain the best Op-Amp performances for the MDAC. In this study, six performances are considered, direct current gain, unity-gain bandwidth, phase margin, power consumption, area, slew rate, thermal noise, and signal to noise ratio. The Matlab optimization toolbox is used to implement the program. Simulations were performed by using Cadence Virtuoso Spectre circuit simulator in standard AMS 0.18μm CMOS technology. A goodagreement is observed between the results obtained bythe program optimization and simulation, after that the Op-Ampis introduced in the MDAC circuit to extract its performances.