Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Propagation of elastic waves in embedded guides: Forward model and imaging with sampling method

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Méthodes CND



Epitaxial growth and nanosecond laser annealing of GeSn/SiGeSn heterostructures

Département technologies silicium (LETI)




Since 2015, CEA LETI has the capacity of depositing GeSn/SiGeSn heterostructures on 200 mm substrates. We are currently at the state-of-the-art in several of their application domains. In ordre to fabricate electically-pumped lasers able to operate at room temperature and performant Infra-Red photodetectors, we will explore during this PhD thesis the n-type and p-type doping of such layers, be it by ion implantation or in-situ during the epitaxial growth itself. In order to take full advantage of those doped layers, we will perform recristallisation and electrical activation anneals. With standard annealing techniques, we would be faced with the significant instability of GeSn / SiGeSn stacks (tin precipitation / surface segregation). This is why we will evaluate, during this PhD thesis, the interest of using nanosecond laser anneals and their impact on the structural and electrical properties of those stacks. Those studies, which will be conducted in our brand new SCREEN-LASSE LT3100 tool, will be among the first ever conducted on this type of semiconductors. We will notably focus on the evolution of cristalline quality, doping level, surface roughness, tin agglomeration / segregation and chemical content with the various process parameters (epitaxy and laser anneals). Such a know-how will be put to good use for the fabrication of innovative optoelectronics devices.

Atomic resolution imagery composition measure applied to superarrays

Département technologies silicium (LETI)

Autre laboratoire



For crystalline materials sensitive to electron beam radiation damage, it is necessary to quantify the chemical composition at the atomic scale while minimizing the electron dose. The usual analytical techniques in the transmission electron microscope (TEM) can not be used because of the high probe current and the relatively long acquisition time. On the other hand, atomic imaging, more precisely using the high-angle annular dark field (STEM-HAADF), is performed at a reduced dose and exhibits contrasts proportional to the atomic number of the elements. In addition, the TEMs Titan on the PFNC are equipped with an aberration corrector to acquire state-of-the-art HAADF images in terms of atomic resolution. However, for the contrast in these images to be quantitatively related to the chemical composition of the material, controlled TEM acquisition conditions and electronic scattering simulations must be developed. In parallel, another imaging technique in the TEM is attracting growing interest: ptychography, or "4D data STEM". This technique, consisting in acquiring a diffraction pattern for each position of the incident electron beam, provide the projected potential in the sample. The development of the quantitative aspect of these imaging techniques has many applications: the one targeted in this thesis is the understanding of the atomic order of GeTe / Sb2Te3 superlattices, materials considered as the most promising for phase change memories (PCRAM).

Hydrogen storage and transport by bio-based liquids

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire Echangeurs et Réacteurs



Climate change is becoming one of the major challenge and concern to be solved in the next years. This target implies a transition from a largely fossil carbon-based energy to a decarbonized energy. In this frame, hydrogen produced by electrolysis of water is an energetic vector of the future. However, efficient hydrogen transport and storage solution are still under study, which account for its lack of large scale development of this technology. In that context, liquid organic hydrogen carriers (LOHC), because of their high volumetric and gravimetric density together with a stable behavior in ambient conditions and safe easy handling, offers promising solutions to overcome hydrogen transport and storage issues. The goal of the thesis is to study hydrogenation/dehydrogenation reactions of innovative and promising compounds in order to validate their use as LOHC.

Measurement of nuclear decay data for beta decay and electron capture using metallic magnetic calorimeters


Laboratoire de Métrologie de l'Activité



In the framework of ionizing radiation metrology, one of the tasks of the Laboratoire National Henri Becquerel (LNHB), the French national laboratory for ionizing radiation metrology, is the precise determination of nuclear decay data. During this PhD thesis, cryogenic detectors will be developed for the precise measurement of the shapes of beta spectra, photon emission probabilities and capture probabilities of radionuclides decaying via electron capture. These data are required in various fields of research and application, including nuclear medicine, nuclear energy and waste management, or neutrino physics research. The PhD student will conduct experiments comprising the conception and fabrication of cryogenic detectors, their operation in a complex cryogenic setup, work with highly specific electronics, Monte Carlo simulations, and data analysis using sophisticated methods. The measured data will be compared with theoretical calculations and help to improve nuclear data tables.

Explaining predicitive-model decisions: towards automatic interpretation of tree-ensemble models


Laboratoire d'Analyse des Données et d'Intelligence des Systèmes



Until recently, the focus in predictive modelling has mainly been set on improving model prediction accuracy. Many successful models scaling to big amounts of heterogeneous data have been proposed in the literature, and widely used implementations of these models are available. Unfortunately, these models generally do not intrinsically come with an easy way to explain their predictions, and are often presented as black-box tools performing complex and non-intuitive operations on their inputs. This can be an issue in many applications where the interpretation of the model decision may have a greater added-value than the decision itself. Examples include medical diagnosis where the interpretation would consist in identfying which combination(s) of characteristics presented by an individual contributes most to the diagnosis. In this thesis, we propose to add interpretability to a specific class of machine learning models known as tree-ensemble models, without impacting the performance of the model we want to interpret. In the continuation of the work already initiated in the laboratory, the objective is to analyze the combinations of input features along with their respective numerical values, so that each instance-level decision taken by the model can be explained by a set of input features having particular numerical values. Fault detection in connected manufacturing provides an interesting application for such approaches, and data as well as the fault detection models will be provided as a starting point for this thesis work.

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