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.

This article deals with the study of a new swimming microrobot behavior using an analytical investigation. The analyzed microrobot is associated by a spherical head and hybrid tail. The principle of modeling is based on solving of the coupled elastic/fluidic problems between the hybrid tail’s deflections and the running environment. In spite of the resulting nonlinear model can be exploited to enhance both the sailing ability and also can be controlled in viscous environment using nonlinear control investigations. The applications of the micro-robot have required the precision of control for targeting the running area in terms of response time and tracking error. Due to these limitations, the Flatness-ANFIS based control is used to ensure a good control behavior in hazardous environment. Our control investigation is coupled the differential flatness and adaptive neuro-fuzzy inference techniques, in which the flatness is used to planning the optimal trajectory and eliminate the nonlinearity effects of the resulting model. In other hand, the neuro-fuzzy inference technique is used to build the law of control technique and minimize the dynamic error of tracking trajectory. In particular, we deduct from a non linear model to an optimal model of the design parameter’s using Multi-Objective genetic algorithms (MOGAs). In addition, Computational fluid dynamics modeling of the microrobot is also carried out to study the produced thrust and velocity of the microrobot displacement taking into account the fluid parameters. Our analytical results have been validated by the recorded good agreement between the numerical and analytical results.