In recent years, the design and fabrication ofmulti-gate Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) have attracted more efforts due to their high appropriateness for advanced integration circuits’ applications. In fact, the boost of MOSFET structures is a battle against parasitic phenomena appearing at the nanoscale level. Short channel and quantum confinement effects are among the critical drawbacks that need to be remedied carefully. On the other hand, the hot carrier degradation effect is mainly a reliability concern affecting the device per- formance after long duration of work. In response to the high computational costs related to the development of physi- cal based models for Double Gate (DG) MOSFETs including all these effects, more flexible alternatives have been proposed for the prediction of device performances. Our aim in this chapter is to investigate the efficiency of a new proposed frame- work, built upon Kriging metamodeling and Non-dominated Sorting Genetic Algorithm version II (NSGA II), for the optimal design in terms of OFF-current, threshold voltage and swing factor. The input variables of interest are limited to the geometrical parameters namely the channel length and thickness. Data generated according to computer experiments, based on ATLAS 2-D simulator, are used to identify and adjust Kriging surrogate models. It is emphasized that the obtained models can be used accurately in a multi-objective context to offer several Pareto optimal configurations. Therefore, a wide range of selection possibilities is avail- able to the designer depending on situations under consideration.

In recent years, the design and fabrication ofmulti-gate Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) have attracted more efforts due to their high appropriateness for advanced integration circuits’ applications. In fact, the boost of MOSFET structures is a battle against parasitic phenomena appearing at the nanoscale level. Short channel and quantum confinement effects are among the critical drawbacks that need to be remedied carefully. On the other hand, the hot carrier degradation effect is mainly a reliability concern affecting the device per- formance after long duration of work. In response to the high computational costs related to the development of physi- cal based models for Double Gate (DG) MOSFETs including all these effects, more flexible alternatives have been proposed for the prediction of device performances. Our aim in this chapter is to investigate the efficiency of a new proposed frame- work, built upon Kriging metamodeling and Non-dominated Sorting Genetic Algorithm version II (NSGA II), for the optimal design in terms of OFF-current, threshold voltage and swing factor. The input variables of interest are limited to the geometrical parameters namely the channel length and thickness. Data generated according to computer experiments, based on ATLAS 2-D simulator, are used to identify and adjust Kriging surrogate models. It is emphasized that the obtained models can be used accurately in a multi-objective context to offer several Pareto optimal configurations. Therefore, a wide range of selection possibilities is avail- able to the designer depending on situations under consideration.

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.