Neural network based equalizers can easily compensate channel impairments; such additive noise and inter symbol interference (ISI). The authors present a new approach to improve the training efficiency of the multilayer perceptron (MLP) based equalizer. Their improvement consists on modifying the back propagation (BP) algorithm, by adapting the activation function in addition to the other parameters of the MLP structure. The authors report on experiment results evaluating the performance of the proposed approach namely the back propagation with adaptive activation function (BPAAF) next to the BP algorithm. To further prove its effectiveness, the proposed approach is also compared beside a so known nonlinear equalizer explicitly the multilayer perceptron with decision feedback equalizer MLPDFE. The authors consider various performance measures specifically: signal resorted quality, lower steady state MSE reached and minimum bit error rate (BER) achieved, where nonlinear channel equalization problems are employed.

Neural network based equalizers can easily compensate channel impairments; such additive noise and inter symbol interference (ISI). The authors present a new approach to improve the training efficiency of the multilayer perceptron (MLP) based equalizer. Their improvement consists on modifying the back propagation (BP) algorithm, by adapting the activation function in addition to the other parameters of the MLP structure. The authors report on experiment results evaluating the performance of the proposed approach namely the back propagation with adaptive activation function (BPAAF) next to the BP algorithm. To further prove its effectiveness, the proposed approach is also compared beside a so known nonlinear equalizer explicitly the multilayer perceptron with decision feedback equalizer MLPDFE. The authors consider various performance measures specifically: signal resorted quality, lower steady state MSE reached and minimum bit error rate (BER) achieved, where nonlinear channel equalization problems are employed.

Neural network based equalizers can easily compensate channel impairments; such additive noise and inter symbol interference (ISI). The authors present a new approach to improve the training efficiency of the multilayer perceptron (MLP) based equalizer. Their improvement consists on modifying the back propagation (BP) algorithm, by adapting the activation function in addition to the other parameters of the MLP structure. The authors report on experiment results evaluating the performance of the proposed approach namely the back propagation with adaptive activation function (BPAAF) next to the BP algorithm. To further prove its effectiveness, the proposed approach is also compared beside a so known nonlinear equalizer explicitly the multilayer perceptron with decision feedback equalizer MLPDFE. The authors consider various performance measures specifically: signal resorted quality, lower steady state MSE reached and minimum bit error rate (BER) achieved, where nonlinear channel equalization problems are employed.

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.

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.