14 research outputs found
RĂ©solution dâun problĂšme inverse par une approche neuronale. Application Ă la caractĂ©risation de rĂ©seaux optiques
International audienc
Caractérisation optique de réseaux de diffraction submicroniques par des techniques génétique et neuronale
Les efforts consentis dans les annĂ©es rĂ©centes dans la conception de rĂ©seaux de diffraction de pĂ©riode rĂ©duite ont conduit Ă atteindre les limites de rĂ©solution des diffĂ©rents techniques de caractĂ©risation microscopiques utilisĂ©es jusqu'alors. De nouvelles mĂ©thodes doivent ĂȘtre mises au point afin de permettre une bonne adĂ©quation entre les formes espĂ©rĂ©es et celles effectivement rĂ©alisĂ©es. Une solution efficace consiste Ă rĂ©soudre le problĂšme inverse de la diffraction. Ainsi, il est possible de remonter au profil du rĂ©seau connaissant un certain nombre d'efficacitĂ©s diffractĂ©es appelĂ© signature optique sous un certaines hypothĂšses. Nous avons montrĂ© expĂ©rimentalement l'efficacitĂ© de deux mĂ©thodes basĂ©es d'une part sur l'utilisation des algorithmes gĂ©nĂ©tiques et d'autre part sur les rĂ©seaux neuronaux. La premiĂšre se rĂ©vĂšle ĂȘtre d'une trĂšs grande robustesse et flexibilitĂ©. Le traitement de la seconde est extrĂȘmement rapide et peut ainsi permettre un contrĂŽle de l'homogĂ©nĂ©itĂ© d'un rĂ©seauRecent development in the field of grating fabrication with submicrometer period leads to reach the resolution limit of classical microscopic caracterization techniques. New methods need to be finalised to produce a good checking of the realised structures. The resolution of the inverse scattering problem can be a good alternative. Thus, it is possible to reconstruct the grating profile from a set of diffracted efficiencies orders called optical signature under some hypothesis. The efficiency of two methods based respectively on the use of a genetic algorithm and a neural network is experimentally demonstrated. The first one is a robust and flexible technique. On the other hand, the speed of the second one permits to accomplish a control of the grating homogeneityST ETIENNE-BU Sciences (422182103) / SudocSudocFranceF
CaractĂ©risation de rĂ©seaux optiques par lâutilisation de rĂ©seaux neuronaux
International audienc
Réduction du temps de contrÎle de réseaux de diffraction submicroniques
International audienc
Diffraction d'une onde plane par un guide périodique (application à la conception d'un filtre en longueur d'onde indépendant de la polarisation)
Cette thÚse est consacrée à la conception d'un filtre en espace libre, premier étage d'un composant plus complexe, capable de réaliser des fonctions DWDM. En particulier, on s'intéresse à l'étude des filtres résonants à modes guidés, ou GMRF, indépendants de la polarisation. Le 1er chapitre donne un état de l'art de ces vingt derniÚres années : la présentation des différentes formes de GMRF et de leur fonctionnement vise à définir une méthodologie permettant de concevoir des structures qui répondent à un gabarit de filtrage donné. Dans un 2e chapitre, on de veloppe une théorie vectorielle rigoureuse de la diffraction d'une onde plane par une surface périodique 1D, la Modal Method with Fourier Expansion. Le 3e chapitre précise les conditions d'indépendance en polarisation des GMRF 1D. On montre la nécessité d'exciter simultanément deux modes guidés, puis on analyse les différentes configurations envisageables, en incidence classique puis conique. L'étude des pÎles de la matrice de diffraction permet d'analyser les couplages résultants qui existent entre les deux modes et permet de déterminer la sensibilité de l'indice effectif des modes aux différents paramÚtres. Cette étude permet de déterminer le triplet {hauteur, facteur de remplissage, indice optique} relatif au réseau, pour lequel le GMRF est indépendant de la polarisationCLERMONT FD-BCIU Sci.et Tech. (630142101) / SudocSudocFranceF
Characterization of optical diffraction gratings by use of a neural method
International audienceOptical scatterometry by use of a neural network is now recognized as an efficient method for retrieving dimensions of gratings in semiconductors or glasses. For an on-line control, a small number of measurements and a rapid data treatment are needed. We demonstrate that these requirements can be met by combining data preprocessing and a proper neural learning method. A good accuracy is attainable with the measurement of only a few orders, even in the presence of experimental errors, with a reduction in learning and computing time
CaractĂ©risation de rĂ©seaux optiques par lâutilisation de rĂ©seaux neuronaux
International audienc
Investigation of Intense Precipitation from Tropical Cyclones during the 21st Century by Dynamical Downscaling of CCSM4 RCP 4.5
In this article, a dynamical downscaling (DD) procedure is proposed to downscale tropical cyclones (TCs) from a general circulation model, with the goal of investigating inland intense precipitation from these storms in the future. This DD procedure is sequential as it is performed from the large scale to the small scale within a one-way nesting modeling framework with the Weather Research and Forecasting (WRF) model. Furthermore, it involves a two-step validation process to ensure that the model produces realistic TCs, both in terms of their general properties and in terms of their intense precipitation statistics. In addition, this procedure makes use of several algorithms such as for the detection and tracking of TCs, with the objective of automatizing the DD process as much as possible so that this approach could be used to downscale massively many climate projections with several sets of model options. The DD approach was applied to the Community Climate System Model (CCSM) version 4 using Representative Concentration Pathway (RCP) 4.5 during the period 2005⁻2100, and the resulting TCs and their intense precipitation were examined
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Investigation of Intense Precipitation from Tropical Cyclones during the 21st Century by Dynamical Downscaling of CCSM4 RCP 4.5.
In this article, a dynamical downscaling (DD) procedure is proposed to downscale tropical cyclones (TCs) from a general circulation model, with the goal of investigating inland intense precipitation from these storms in the future. This DD procedure is sequential as it is performed from the large scale to the small scale within a one-way nesting modeling framework with the Weather Research and Forecasting (WRF) model. Furthermore, it involves a two-step validation process to ensure that the model produces realistic TCs, both in terms of their general properties and in terms of their intense precipitation statistics. In addition, this procedure makes use of several algorithms such as for the detection and tracking of TCs, with the objective of automatizing the DD process as much as possible so that this approach could be used to downscale massively many climate projections with several sets of model options. The DD approach was applied to the Community Climate System Model (CCSM) version 4 using Representative Concentration Pathway (RCP) 4.5 during the period 2005â»2100, and the resulting TCs and their intense precipitation were examined
Estimation of local error by a neural model in an inverse scattering problem
International audienc