The representation of physical processes by a climate model depends on its structure, numerical schemes, physical parameterizations and resolution, with initial conditions and future emission scenarios further affecting the output. The extent to which climate models agree is therefore of great interest, often with greater confidence in robust results across models. This has led to climate model output being analysed as ensembles rather than in isolation, and quantifying the sources of variation across these ensembles is the aim of many recent studies. Statistical attempts to do this include the use of variants of the mixed-effects analysis of variance or covariance (mixed-effects ANOVA/ANCOVA). This work usually focuses on identifying variation in a variable of interest that is due to differences in model structure, carbon emissions scenario, etc. Quantifying such variation is important in determining where models agree or disagree, but further statistical approaches can be used to diagnose the reasons behind the agreements and disagreements by representing the physical processes within the climate models. A process-based approach is presented that uses simulation with statistical models to perform a global sensitivity analysis and quantify the sources of variation in multi-model ensembles. This approach is a general framework that can be used with any generalised linear mixed model (GLMM), which makes it applicable to use with statistical models designed to represent (sometimes complex) physical relationships within different climate models. The method decomposes the variation in the response variable into variation due to 1) temporal variation in the driving variables, 2) variation across ensemble members in the distributions of the driving variables, 3) variation across ensemble members in the relationship between the response and the driving variables, and 4) variation unexplained by the driving variables. The method is used to quantify the extent to which, and diagnose why, precipitation varies across and within the members of two different climate model ensembles on various different spatial and temporal scales. Change in temperature in response to increased CO2 is related to change in global-mean annual-mean precipitation in a multi-model ensemble of general circulation models (GCMs). A total of 46% of the variation in the change in precipitation in the ensemble is found to be due to the differences between the GCMs, largely because the distribution of the changes in temperature varies greatly across different GCMs. The total variation in the annual-mean change in precipitation that is due to the differences between the GCMs depends on the area over which the precipitation is averaged, and can be as high as 63%. The second climate model ensemble is a perturbed physics ensemble using a regional climate model (RCM). This ensemble is used for three different applications. Firstly, by using lapse rate, saturation specific humidity and relative humidity as drivers of daily-total summer convective precipitation at the grid-point level over southern Britain, up to 8% of the variation in the convective precipitation is found to be due to the uncertainty in RCM parameters. This is largely because given atmospheric conditions lead to different rates of precipitation in different ensemble members. This could not be detected by analysing only the variation across the ensemble members in mean precipitation rate (precipitation bias). Secondly, summer-total precipitation at the grid-point level over the British Isles is used to show how the values of the RCM parameters can be incorporated into a GLMM to quantify the variation in precipitation due to perturbing each individual RCM parameter. Substantial spatial variation is found in the effect on precipitation of perturbing different RCM parameters. Thirdly, the method is extended to focus on extreme events, and the simulation of extreme winter pentad (five-day mean) precipitation events averaged over the British Isles is found to be robust to the uncertainty in RCM parameters.

Au sein de la course industrielle à la miniaturisation et avec l’augmentation des exigences technologiques visant à obtenir plus de performances sur moins de surface, la fiabilité des transistors MOSFET est devenue un sujet d’étude de plus en plus complexe. Afin de maintenir un rythme de miniaturisation continu, des nouvelles architectures de transistors MOS en été introduite, les technologies conventionnelles sont remplacées par des technologies innovantes qui permettent d'améliorer l'intégrité électrostatique telle que la technologie FDSOI avec des diélectriques à haute constante et grille métal. Malgré toutes les innovations apportées sur l’architecture du MOS, les mécanismes de dégradations demeurent de plus en plus prononcés. L’un des mécanismes le plus critique des technologies avancées est le mécanisme de dégradation par porteurs chauds (HCI). Pour garantir les performances requises tout en préservant la fiabilité des dispositifs, il est nécessaire de caractériser et modéliser les différents mécanismes de défaillance au niveau du transistor élémentaire. Ce travail de thèse porte spécifiquement sur les mécanismes de dégradations HCI des transistors 28nm FDSOI. Basé sur l’énergie des porteurs, le modèle en tension proposé dans ce manuscrit permet de prédire la dégradation HC en tenant compte de la dépendance en polarisation de substrat incluant les effets de longueur, d’épaisseur de l’oxyde de grille ainsi que l’épaisseur du BOX et du film de silicium. Ce travail ouvre le champ à des perspectives d’implémentation du model HCI pour les simulateurs de circuits, ce qui représente une étape importante pour anticiper la fiabilité des futurs nœuds technologiques. / As the race towards miniaturization drives the industrial requirements to more performances on less area, MOSFETs reliability has become an increasingly complex topic. To maintain a continuous miniaturization pace, conventional transistors on bulk technologies were replaced by new MOS architectures allowing a better electrostatic integrity such as the FDSOI technology with high-K dielectrics and metal gate. Despite all the architecture innovations, degradation mechanisms remains increasingly pronounced with technological developments. One of the most critical issues of advanced technologies is the hot carrier degradation mechanism (HCI) and Bias Temperature Instability (BTI) effects. To ensure a good performance reliability trade off, it is necessary to characterize and model the different failure mechanisms at device level and the interaction with Bias Temperature Instability (BTI) that represents a strong limitation of scaled CMOS nodes. This work concern hot carrier degradation mechanisms on 28nm transistors of the FDSOI technology. Based on carrier’s energy, the energy driven model proposed in this manuscript can predict HC degradation taking account of substrate bias dependence (VB) including the channel length effects (L), gate oxide thickness (TOX) , back oxide BOX (TBox) and silicon film thickness (TSI ). This thesis opens up new perspectives of the model Integration into a circuit simulator, to anticipate the reliability of future technology nodes and check out circuit before moving on to feature design steps.

De nombreuses applications industrielles spécifiques dans les secteurs tels que l'automobile, le médical et le spatial, requièrent un très haut niveau de fiabilité. Ce type d'applications fonctionnant sous des contraintes sévères (haute température, corrosion, vibration, radiations,…) impose aux industriels des spécifications particulières en termes de fiabilité et de consommation d'énergie. Dans ce contexte, les travaux menés ont pour objectif d'étudier la fiabilité des mémoires Flash embarquées pour des applications faible consommation et à forte contrainte de fiabilité. Après une introduction orientée sur les deux volets d'étude que sont la caractérisation électrique et le test de mémoires non volatiles, un modèle physique capable de modéliser le courant de SILC a été développé. Cet outil permet de répondre à la problématique de perturbations en lecture (read disturb) et donne aux designers et technologues un moyen d'estimer le taux de défaillance de cellules mémoires en fonction de paramètres physiques, géométriques et électriques ainsi que des moyens d'action afin de minimiser ce phénomène indésirable. La fiabilité (oxyde tunnel, endurance) et les performances (consommation énergétique) de la cellule Flash sont ensuite étudiées en explorant les variations de paramètres du procédé de fabrication et des conditions électriques de fonctionnement. Enfin, une étude originale menée en temps réel sur plus de 15 mois est consacrée à la fiabilité en rétention des mémoires Flash soumises aux effets des particules radiatives présentes dans l'environnement naturel terrestre. / Many specific applications used in automotive, medical and spatial activity domains, require a very high level of reliability. These kinds of applications, working under severe constraints (high temperature, corrosion, vibration, radiations…) challenge memory manufacturers and impose them particular specifications in terms of reliability and energy consumption. In this context, work presented in this thesis aim at studying embedded Flash memories reliability for low power and high reliability applications. After an introduction oriented on areas of electrical characterizations and Test of non-volatile memories, a physical model of SILC leakage current is developed. This tool is used to answer to disturbs problematic and gives to designers and technologists a way to estimate the failure rate of memory cells according to physical, geometrical and electrical parameters, giving leads to minimize this unwanted phenomenon. Reliability (tunnel oxide, cell endurance) and performances (energy consumption) of Flash memory cell are then studied exploring process parameters variations and electrical conditions optimizations. Finally, an original real-time experiment over more than 15 months is focused on Flash memories retention reliability due to irradiative particles effects of natural terrestrial environment.