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.