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Galileo Galilei once quoted: “Measure what is measurable, and make measurable what is not so”. In silicon manufacturing R&D phase, it often happens that engineers would like to access some parameter values that are not easy, even impossible to measure. When looking at a CDSEM image, the parameters of interest seem easy to extract but in practice getting access to them in a robust and reliable way is not always simple. Developing a contour-based metrology tool coupling robust contour extraction with a comprehensive contour metrology environment could help to bridge this gap. In previous works, it has been shown that SEM images contain significant amounts of information that can be extracted and analyzed using efficient contour extraction and analysis toolboxes [1, 2]. Also, the concept of implementing remote contour-based metrology has been introduced. The present work continues to unveil what can be achieved with such solutions. For that, the example of implant layers’ process assumption will be explored. During this process step, counter doping problems can occur for example when the distance between layers deviates from nominal. Therefore, it is crucial for design rule control to measure some critical dimensions such as minimum distance between layers, corner rounding, slope, etc. However, given the characteristics of the different structures in the images, which may come from different layers and/or processes steps, the measurements are not straightforward to extract with standard CDSEM metrology algorithms. Moreover, recipes are complex to setup, measurements by themselves are not very stable, and usually an indirect determination of the key figure is performed. In this paper, we will show that multilayer contour-based metrology, mixing image contour and GDS layout, allows to overcome the previously mentioned difficulties, as well as to generate measurements that are not possible to be performed by using standard algorithms.

In semiconductor fabs, electron microscopes are key equipment for metrology, failure analysis, physical characterization and defect review classification. In a wafer fab like ST Crolles 300mm, CDSEMs are generating more than 20 Million of images per year. The image is by itself a raw material on which the metrology is performed. It is needed to get access to CD which is very often a single value extracted. If the CD is in specification, it is very unlikely that someone will look at the picture. If someone would do so in a systematic way, it would see that there is much more information available in the image than a single CD value. Unfortunately, most of this information passes under the radar of SPC charts and is somehow wasted. This paper presents results obtained by CDSEM image contour analysis from various kind of technologies and applications in manufacturing in our fab. These results show that images contain significant amounts of information that can be extracted and analyzed using an efficient contour extraction and analysis toolbox. Process variability of complex shapes can be shown, robust layer to layer metrics can be computed, pattern shifting, shape changes, image quality and many others too. This opens new possibilities for process control and process variability monitoring and mitigation.

In this work, we evaluate through rigorous coupled-wave analysis simulations the effect of various process-related inaccuracies on plasmonic filters performance, especially regarding cross-shaped-hole arrays. Focusing exclusively on CMOS-compatible materials, we demonstrate the potential of these structures for reliable integration and fabrication at wafer level. A high monitoring of the deposition parameters is required to control the transmission level and the resonant wavelength of the filters. Optical proximity calculations show that double patterning is a way to limit corner rounding and to avoid losing the resonant mode of the crosses. The impacts on plasmon resonances of the metal oxidation, or a sloped profile after metal etching, are evaluated. These results allow for a good anticipation regarding process issues to realize efficient plasmonic filters.

Image sensors are now part of our daily life through smartphones and other mobile devices. The increasing quality of images is linked to the important progress of technologies. Imaging technologies allow the association of imaging capture modules and integrated circuits. Those modules are built with photosensibles matrix that react to specific wavelength according to the application. The matrix need the fabrication of microlenses that make possible the capture and focalisation of light toward each pixel. Firstly, the student will characterise the resin used in microlenses fabrication in order to create a model that can simulate the resin's response. A methodology of design and preparation specific to grayscale masks also need to be established. In order to do that, several iterations of mask fabrication alongside 3D caracterisations will be needed. Their purpose is to check the performance and precision of modelling tools composing this fabrication process. Once the methodoly established, it will be used to propose 3D shapes of effective and innovating microlenses while investigating the key parameters such as shape, mask type, the fine optimization of lithography process, optimisation of materials (reflectivity, piling, films thickness...). In a long term vision, this project will be used to create a comparison of microlenses prformances during the thesis with the industrial reference process to show the benefit of such model., Les capteurs d'images font désormais partie de notre quotidien à travers les smartphones et autres appareils mobiles. La qualité croissante des images délivrées est liée au progrès importants des technologies mises en oeuvre. Les technologies Imageur permettent d'associer des modules de capture d'image avec des circuits intégrés. Ces modules sont construits avec des matrices de pixels photosensibles qui réagissent à certaines longueurs d'onde en fonction du type d'application visé. Ces matrices nécessitent la fabrication de microlentilles qui permettent de capturer et de focaliser les rayons lumineux vers chaque pixel. Dans un premier temps l'étudiant(e) aura comme but de caractériser les résines propres à la fabrication des microlentilles et de créer un modèle pour simuler la réponse de la résine. Une méthodologie de design et de préparation de données spécifique aux masques de type Grayscale devra également être établie. Pour ce faire, plusieurs itérations de fabrication de masque couplées à des caractérisations 3D seront nécessaires pour vérifier la performance et la précision des outils de modélisation composant ce processus de fabrication. Une fois cette méthodologie établie, elle sera utilisée afin de proposer des formes 3D de microlentilles performantes et innovantes, en investiguant tous les paramètres clefs tels que la forme et le type de masque, une optimisation fine du ou des procédé(s) de lithographie, l'optimisation des matériaux utilisés (réflectivité, empilement, épaisseurs des couches...). Dans une vision à plus long terme, ce projet permettra de faire un comparatif de performance des microlentilles réalisées durant la thèse avec le procédé de référence industriel pour démontrer le gain d'une telle méthodologie.