Afin de respecter sa feuille de route, l’industrie du semi conducteur propose des nouvelles générations de technologies (appelées nœuds technologiques) tous les deux ans. Ces technologies présentent des dimensions de motifs de plus en plus réduites et par conséquent des contrôles des dimensions de plus en plus contraints. Cette réduction des tolérances sur les résultats métrologiques entraine forcément une évolution des outils de métrologie dimensionnelle. Aujourd’hui, pour les nœuds les plus avancés, aucune technique de métrologie ne peut répondre aux contraintes imposées. Les limitations se situent aussi bien sur les principes mêmes des méthodes employées que sur la quantité nécessaire de données permettant une analyse poussée ainsi que le temps de calcul nécessaire au traitement de ces données. Dans un contexte industriel, les aspects de rapidité et de précision des résultats de métrologie ne peuvent pas être négligés, de ce fait, une nouvelle approche fondée sur de la métrologie hybride doit être évaluée. La métrologie hybride consiste à mettre en commun différentes stratégies afin de combiner leurs forces et limiter leurs faiblesses. L’objectif d’une approche hybride est d’obtenir un résultat final présentant de meilleures caractéristiques que celui obtenu par chacune des techniques séparément. Cette problématique de métrologie hybride peut se résoudre par l’utilisation de la fusion de données. Il existe un grand nombre de méthodes de fusion de données couvrant des domaines très variés des sciences et qui utilisent des approches mathématiques différentes pour traiter le problème de fusion de données. L’objectif de ce travail de thèse est de développer cette problématique de métrologie hybride et fusion de données dans le cadre d’une collaboration entre deux laboratoires : LTM/ CNRS ( Laboratoire des Technologies de la Microélectronique) et le LETI/CEA (Laboratoire d’Electronique et de Technologies de l’Information). Le concept de la fusion de données est présenté dans un contexte de métrologie hybride appliquée au domaine de la microélectronique. L’état de l’art au niveau des techniques de métrologie est présenté et discuté. En premier lieu, le CD SEM pour ces caractéristiques associant rapidité et non destructibilité, ensuite l’AFM pour sa vision juste des profils des motifs et enfin la scattérométrie pour ses aspects de précision de mesures et sa rapidité tout en conservant une approche non destructive. Le FIB-STEM, bien que destructif, se positionne sur une approche de technique de référence. Les forces et les faiblesses de ces différentes méthodes sont évaluées afin de pouvoir les introduire dans une approche de métrologie hybride et d’identifier le rôle que chacune d’entre elle peut jouer dans ce contexte. Plusieurs campagnes de mesures ont été réalisées durant cette thèse afin d’apporter des connaissances sur les caractéristiques et les limitations de ces techniques et pouvoir les inclure dans différents scénarii de métrologie hybride. La méthode retenue pour la fusion de données est fondée sur une approche Bayesienne. Cette méthode a été évaluée dans un contexte expérimental cadré par un plan d’expérience permettant la mesure de la hauteur et la largeur de lignes en combinant différentes techniques de métrologie. Les données collectées ont été exploitées pour les étapes de debiaisage mais également pour un déroulement complet de fusion et dans les deux cas, la métrologie hybride montre les avantages de cette approche pour améliorer la justesse et la précision des résultats. Avec la poursuite d’un développement poussé, la technique de métrologie hybride présentée ici semble donc pouvoir s’intégrer dans un processus de fabrication dans l’industrie du semi conducteur. Son application n’est pas seulement destinée à de la métrologie dimensionnelle mais peut fournir également des informations sur la calibration des équipements. / The industry of semiconductors continues to evolve at a fast pace, proposing a new technology node around every two years. Each new technology node presents reduced feature sizes and stricter dimension control. As the features of devices continue to shrink, allowed tolerances for metrology errors must shrink as well, pushing the evolution of the metrology tools.No individual metrology technique alone can answer the tight requirements of the industry today, not to mention in the next technology generations. Besides the limitations of the metrology methods, other constraints such as the amount of metrology data available for higher order analysis and the time required for generating such data are also relevant and impact the usage of metrology in production. For the production of advanced technology nodes, neither speed nor precision may be sacrificed, which calls for cleverer metrology approaches, such as the Hybrid Metrology.Hybrid Metrology consists of employing different metrology strategies together in order to combine their strengths while mitigating their weaknesses. This hybrid approach goal is to improve the measurements in such a way that the final data presents better characteristics that each method separately. One of the techniques than can be used to combine the data coming from different metrology techniques is called Data Fusion. There are a large number of developed methods of Data Fusion, using different mathematical tools, to address the data fusion process.The first goal of this thesis project was to start developing the topics of Data Fusion and Hybrid Metrology within the two laboratories whose cooperation made this work possible: LTM (Laboratoire des Technologies de la Microélectronique) and LETI (Laboratoire d'électronique et de technologie de l'information). This thesis presents the concepts of Data Fusion in the context of Hybrid Metrology applied to dimensional measuring for the semiconductors industry. This concept can be extensively used in many other fields of applications.In this work the basics of state-of-the-art metrology techniques is presented and discussed. The focus is the CD-SEM, for its fast and almost-non-destructive metrology; the AFM, for its accurate profile view of patterns and non-destructive characteristic; the Scatterometry, for its precision, global and fast measurements; and the FIB-STEM, as a reference on accuracy for any type of profile, although destructive. The strengths and weaknesses of these methods were discussed in order to introduce the need of Hybrid Metrology and to identify the role that each of those methods can play in this context.Several experiments were performed during this thesis work in order to provide further knowledge about the characteristics and limitations of each metrology method and to be used as either inputs or reference on the different Hybrid Metrology scenarios proposed.The selected method for fuse the data coming from different metrology methods was the Bayesian approach. This technique was evaluated in different experimental contexts, both for Height and CD metrology combining different metrology methods. Results were evaluated for both the debiasing step alone and for the complete fusion flow. In both cases, it was clear the advantages of using a Hybrid Metrology approach for improving the measurement precision and accuracy.The presented Hybrid Metrology technique may be used by the semiconductor industry in different steps of the fabrication process. This technique can also provide information for machine calibration, such as a CD-SEM tool being calibrated based on Hybrid Metrology results generated using the CD-SEM itself together with Scatterometry data.

Este trabalho tem por finalidade mostrar a aplicação e a utilização de um aeromodelo elétrico de asa fixa, também conhecido como veículo aéreo não tripulado (VANT), com controle manual ou automático, para coleta de dados e imagens em propriedades rurais, com a premissa de auxiliar os gestores no processo de gestão e tomada de decisão. A metodologia utilizada para a realização das coletas foi feita por meio de voos programados em dias e condições diferentes, para verificação e análise de desempenho do aeromodelo. Os resultados obtidos com os voos foram acima do esperado, gerando excelentes imagens e dados confiáveis. Sendo assim, pôde-se concluir que a utilização de VANTs, em coletas de dados e imagens em propriedades rurais foi satisfatória e auxiliou os gestores no processo de gerenciamento e rotacionamento de animais no pasto, uma vez que as imagens permitiram uma boa visualização e o aeromodelo desenvolvido cumpriu o seu objetivo com bom desempenho e agilidade. / This study aims to show the application and the use of a fixed-wing electric model aircraft, also known as unmanned aerial vehicle (UAV), with manual or automatic control, for collecting data and images in rural properties, with the premise of assisting managers in the management process and decision making. The methodology used to carry out the collection was made through scheduled flights on different days and conditions, for verification and performance analysis of the model aircraft. The results obtained with the flight were higher than expected, generating excellent images and reliable data. Thus, we can conclude that the use of UAV, in data and images collection in rural properties has been satisfactory and has assisted managers in the process of management and rotation of animals on pasture, since the images have allowed a preview of the terrain, with sharp images and the model aircraft has fulfilled its goal with a good performance and agility.

Interpretar a enorme quantidade de dados coletados, entender as causas e propor estratégias para gerenciar a variabilidade do campo, freqüentemente são apontados como alguns dos principais problemas para o avanço da agricultura de precisão, AP. Os sistemas de informação tornam-se fundamentais na solução desses problemas, mas apesar de existirem muitos pacotes de software disponíveis no mercado, variando de muito simples a muito sofisticados e diversos sistemas originados de experiências de pesquisas, a natureza proprietária e monolítica das soluções impedem o uso em larga escala. A AP envolve uma grande variedade de técnicas de análise, fontes e formatos de dados, perfis de usuário, e muitos outros aspectos que tornam uma aplicação muito complexa do ponto de vista da engenharia de software. Com o objetivo de fornecer a base para o desenvolvimento de sistemas de informação para AP baseados em padrões abertos e plataformas de software livre, uma infra-estrutura de desenvolvimento de sistemas de informação para a agricultura de precisão é proposta. Com base nas idéias seminais dessa proposta, são desenvolvidos protótipos para a condução de experimentos, os quais exploram caminhos de evolução para a infra-estrutura, com especial atenção sobre aspectos de arquitetura de software. Como estudo de caso, uma aplicação web que realiza filtragem de dados de monitores de produtividade é apresentada. Usando a metodologia de desenvolvimento em espiral, sucessivas versões dessa aplicação foram criadas e os resultados usados para propor melhoramentos à infra-estrutura. A infra-estrutura final contém cinco componentes: uma arquitetura de referência para sistemas de informação orientados a serviços para AP, uma linguagem padrão para troca de dados entre serviços agrícolas, um barramento para conexão de serviços agrícolas, um provedor de serviços geoespaciais e um portal para serviços agrícolas. Ela se mostrou adequada para a criação de sistemas de informação para AP interoperáveis, de baixo custo e com capacidade de evolução, mudando o paradigma dos sistemas para AP preponderantemente proprietários e monolíticos para abertos e orientados a serviços distribuídos. / Interpreting the huge amount of data collected, understanding the causes and being able to propose sound strategies for the field variability management are frequently pointed out as major issues for the advance of precision agriculture. Therefore, the information systems become fundamental for the solution of these problems. Although there are many available software packages in the market, varying from simple to very sophisticated and diverse systems deriving from experiences of research, the monolithic and proprietary nature of the solutions hinder their use in wide scale. Precision agriculture involves a great variety of techniques of data analysis, data sources, data formats, user profiles, and many other aspects that make it a complex application from the software engineering point of view. Aiming to supply the base for the development of open standards-based precision agriculture information systems and free software platforms, an infrastructure for developing precision agriculture information systems is proposed. Based on the fundamentals of that proposal, prototypes are developed which explore different evolutionary paths for the infrastructure, with special attention to software architecture aspects. As a case study, a yield monitor data filtering web application is presented. Using the spiral development methodology, successive versions of this application were created and the results used to improve the infrastructure. The final infrastructure contains five components: a service-oriented reference architecture for precision agriculture information systems, a standard language for data exchange between agricultural services, a service bus for connecting agricultural services, a geospatial services provider, and an agricultural services portal. It revealed to be adequate for the creation of precision agriculture interoperable systems, of low cost and with capacity for evolution, moving the paradigm of systems for AP preponderantly monolithic and proprietary to open and distributed service-oriented.

Accurate forecasts of weather and climate will become increasingly important as the world adapts to anthropogenic climatic change. Forecasts' accuracy is limited by the computer power available to forecast centres, which determines the maximum resolution, ensemble size and complexity of atmospheric models. Furthermore, faster supercomputers are increasingly energy-hungry and unaffordable to run. In this thesis, a new means of making computer simulations more efficient is presented that could lead to more accurate forecasts without increasing computational costs. This 'scale-selective reduced precision' technique builds on previous work that shows that weather models can be run with almost all real numbers represented in 32 bit precision or lower without any impact on forecast accuracy, challenging the paradigm that 64 bits of numerical precision are necessary for sufficiently accurate computations. The observational and model errors inherent in weather and climate simulations, combined with the sensitive dependence on initial conditions of the atmosphere and atmospheric models, renders such high precision unnecessary, especially at small scales. The 'scale-selective' technique introduced here therefore represents smaller, less influential scales of motion with less precision. Experiments are described in which reduced precision is emulated on conventional hardware and applied to three models of increasing complexity. In a three-scale extension of the Lorenz '96 toy model, it is demonstrated that high resolution scale-dependent precision forecasts are more accurate than low resolution high-precision forecasts of a similar computational cost. A spectral model based on the Surface Quasi-Geostrophic Equations is used to determine a power law describing how low precision can be safely reduced as a function of spatial scale; and experiments using four historical test-cases in an open-source version of the real-world Integrated Forecasting System demonstrate that a similar power law holds for the spectral part of this model. It is concluded that the scale-selective approach could be beneficially employed to optimally balance forecast cost and accuracy if utilised on real reduced precision hardware.

This thesis deals with the new unconventional way of agricultural land management. Currently, high precision of work machines in the field is required. The development and accessibility of modern agricultural equipment linked to the information technology has led this branch to the unnecessary and high precise field ecosystem management. This new approach is called as precision agriculture. In the theoretical part of work is made the literature research and overview of the available data about the precision farming methods and the possibilities of field observation and data collection, including their processing with the available high information technology. The practical part evaluates the current possibilities of precision farming in the Czech Republic and in the world and looks for a solution to its implementation in the specific business environment of the agricultural laboratory.

Soil landscape characterization into landform elements for precision agriculture has become an important issue. As soil properties and crop yields change over the landscape, delineating landform elements as a basis for site-specific application of crop inputs has become a reality. Two different methods of delineating landform elements from agricultural fields were tested and compared. The first method delineated landform elements from digital elevation maps with the use of the LandMapR(tm) software, the second method delineated classes from IKONOS high resolution panchromatic images using an unsupervised classification algorithm. The LandMapR(tm) model delineated landform elements from true elevation data collected in the field and was considered the reference dataset to which the image classification maps were compared to. The IKONOS imagery was processed using a combination of one filtering algorithm and one unsupervised classification method prior to being compared to the classified DEM. A total of 20 filtering algorithms and two unsupervised methods were used for each of the five study sites. The study sites consisted of four agricultural fields covered with crop stubble and one field in summer fallow. Image classification accuracy assessment was reported as overall, producer’s and user’s accuracy as well as Kappa statistic. Results showed that filtering algorithms and classification methods had no effects on image classification accuracies. Highest classification accuracy of image map to landform element map comparison achieved for all study sites was 17.9 %. Classification accuracy was affected by the heterogeneity of the ground surface cover found in each field. However, the classification accuracy of the fallow field was not superior to the stubble fields. / May 2006

No description available.

Bakgrund: Deep Brain Stimulation (DBS) i nucleus subthalamicus (STN) och i kaudala zona incerta (cZi) har visat positiva effekter på motoriska symtom (rörelseförmåga och balans) vid Parkinsons sjukdom. Efter DBS har dock negativa taleffekter noterats, inklusive nedsatt artikulatorisk precision och stabilitet. Nedsatt artikulatorisk precisionoch stabilitet påverkar klusilproduktionen mest vilket kan resultera i att klusilexplosionen uteblir eller att multipla explosioner uppstår. Mål: Att undersöka artikulatorisk precision och stabilitet vid klusilexplosionen efter DBS i STN respektive cZi. Metod: I studien deltog 19 patienter varav 9 deltagare stimulerats i STN och 10 deltagare i cZi. Talinspelningar gjordes innan operation och ett år efter operation med DBSstimulering på och av. Klusilerna i talmaterialet delades in i tre kategorier, de med en klusilexplosion, de med två eller flera klusilexplosioner samt de utan klusilexplosion. Hypotesprövning gjordes gällande fördelningen av klusiler med multipla explosioner samt klusiler utan explosion mellan och inom patientgrupperna samt inom respektive klusil. Resultat: Multipla explosioner ökade hos cZi-patienterna och minskade hos STNpatienterna. Klusiler som saknade explosion ökade hos STN-patienterna medan de minskade hos cZi-patienterna. För båda patientgrupperna ökade multipla explosioner och klusiler utan explosion då stimuleringen var på jämfört med av. Skillnaderna mellan och inom grupperna var inte signifikanta. Slutsats: Artikulatorisk precision och stabilitet påverkades efter DBS i form av bristande stabilitet, för kort slutningsrörelse och felaktig koordination av slutningsrörelsen. Detta resulterade i multipla explosioner och uteblivna explosioner med olika effekter för elektrodlokalisationerna. / Background: Deep Brain Stimulation (DBS) in Subthalamicus Nucleus (STN) and Caudala zona incerta (cZi) have shown positive effects on motor symptoms in Parkinson‘s disease. Negative effects on speech after DBS has been noted including reduced articulatory precision. Reduced articulatory precision and stability affects the production of stop consonants and as a result, loss of burst or multiple burst can occur. Aim: To investigate articulatory precision and stability regarding the burst in stop consonants after DBS of STN or cZi. Method: The study included 19 patients with 9 patients stimulated in STN and 10 patientsin cZi. Speech recordings were made before surgery and one year after with stimulation ON and OFF. The stop consonants were divided into three categories, those with one burst, those with two or more bursts (multiple burst) and those with loss of burst. Hypothesis testing was done on the categorization of the stop consonants in and between the groups and between the stop consonants types. Results: Multiple burst increased in the cZi group and decreased in the STN group. Stop consonants with loss of burst increased in the STN group but decreased in the cZi group. For both groups multiple burst and stop consonants with loss of burst increased with stimulation on. The differences between and within the groups were not significant. Conclusion: Articulatory precision and stability were affected by DBS with decreased stability, shortened occlusion and incorrect coordination. As a result multiple burst and loss of burst occurred in both groups. / Speech, voice and swallowing outcomes after deep brain stimulation of the zona incerta and the pedunculopontine nucleus in Parkinson’s disease: Comparsion with stimulation of the subthalamic nucleus

Soil landscape characterization into landform elements for precision agriculture has become an important issue. As soil properties and crop yields change over the landscape, delineating landform elements as a basis for site-specific application of crop inputs has become a reality. Two different methods of delineating landform elements from agricultural fields were tested and compared. The first method delineated landform elements from digital elevation maps with the use of the LandMapR(tm) software, the second method delineated classes from IKONOS high resolution panchromatic images using an unsupervised classification algorithm. The LandMapR(tm) model delineated landform elements from true elevation data collected in the field and was considered the reference dataset to which the image classification maps were compared to. The IKONOS imagery was processed using a combination of one filtering algorithm and one unsupervised classification method prior to being compared to the classified DEM. A total of 20 filtering algorithms and two unsupervised methods were used for each of the five study sites. The study sites consisted of four agricultural fields covered with crop stubble and one field in summer fallow. Image classification accuracy assessment was reported as overall, producer’s and user’s accuracy as well as Kappa statistic. Results showed that filtering algorithms and classification methods had no effects on image classification accuracies. Highest classification accuracy of image map to landform element map comparison achieved for all study sites was 17.9 %. Classification accuracy was affected by the heterogeneity of the ground surface cover found in each field. However, the classification accuracy of the fallow field was not superior to the stubble fields.

Soil landscape characterization into landform elements for precision agriculture has become an important issue. As soil properties and crop yields change over the landscape, delineating landform elements as a basis for site-specific application of crop inputs has become a reality. Two different methods of delineating landform elements from agricultural fields were tested and compared. The first method delineated landform elements from digital elevation maps with the use of the LandMapR(tm) software, the second method delineated classes from IKONOS high resolution panchromatic images using an unsupervised classification algorithm. The LandMapR(tm) model delineated landform elements from true elevation data collected in the field and was considered the reference dataset to which the image classification maps were compared to. The IKONOS imagery was processed using a combination of one filtering algorithm and one unsupervised classification method prior to being compared to the classified DEM. A total of 20 filtering algorithms and two unsupervised methods were used for each of the five study sites. The study sites consisted of four agricultural fields covered with crop stubble and one field in summer fallow. Image classification accuracy assessment was reported as overall, producer’s and user’s accuracy as well as Kappa statistic. Results showed that filtering algorithms and classification methods had no effects on image classification accuracies. Highest classification accuracy of image map to landform element map comparison achieved for all study sites was 17.9 %. Classification accuracy was affected by the heterogeneity of the ground surface cover found in each field. However, the classification accuracy of the fallow field was not superior to the stubble fields.