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.

Dans ce travail, nous proposons une nouvelle méthode pour la détection des micro-ondes acoustiques de volume dans un substrat piézoélectrique (Niobate de Lithium LiNbO3). Nous avons utilisé la classification par réseaux probabilistes de neurones comme un moyen d'analyse numérique afin de classer toutes les valeurs de la partie réelle et la partie imaginaire du coefficient d'atténuation pour les différentes vitesses acoustiques en construisant ainsi un modèle de détection efficace des ondes de volume. Cette étude sera très intéressante dans la modélisation et la réalisation des dispositifs micro-ondes acoustiques tels que les dispositifs à ultrasons basé sur la propagation des micro-ondes acoustiques de volume. En plus, nous pouvons appliquer cette technique sur n’importe qu’elle structure piézoélectrique possédant la même classe cristallographique, il suffit juste de changer les paramètres caractéristiques du matériau considéré.