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

PostDocs : selection by topics

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Developement of relaxed pseudo-substrate based on InGaN porosified by electrochemical anodisation

Département des Plateformes Technologiques (LETI)

Laboratoire des Matériaux pour la photonique



Matériaux et procédés émergents pour les nanotechnologies et la microélectronique (.pdf)

As part of the Carnot PIRLE project starting in early 2021, we are looking for a candidate for a post-doctoral position of 24 months (12 months renewable) with a specialty in material science. The project consists in developing a relaxed pseudo-substrate based on III-N materials for µLEDs applications, especially for emission in red wavelength. The work will focus on developing an InGaN-based epitaxy MOCVD growth process, on an innovative substrate based on electrochemically anodized and relaxed materials. He (She) will have characterize both the level of relaxation of the re-epitaxied layer and its crystalline quality. These two points will promote the epitaxial regrowth of an effective red LED. The candidate will be part of the team, working on the PIRLE project, will be associated to the work on red LED growth and its optical and electro-optical characterizations.

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Deep learning for 3D analysis and segmentation of nanocomposites at the nanoscale

Département des Plateformes Technologiques (LETI)

Laboratoire Microscopie Mesures et Défectivité



Nano-caractérisation avancée (.pdf)

LTM and LETI work jointly on the integration of nanocomposites in microelectronic devices. The 3D characterization of these materials is essential to optimize their fabrication and to increase their final performance. The nanotomography techniques available within the Nano Characterization Platform (PFNC) generate very large volumes of data (ranging from 10Gb to more than 100Gb per analysis) and require manual processing for volume segmentation and extraction of quantitative information such as the size of nanoparticles and their 3D distribution within the polymer. Rapid advances in the field of artificial intelligence, in particular deep learning, are now making it possible to automate the processing of such data. We propose here to explore these tools, not only to obtain fast and reliable results with very little human intervention, but also to allow comparative and objective studies on different types of nanocomposites.

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Modeling of trapping and vertical leakage effects in GaN epitaxial substrates on Si

Département Composants Silicium (LETI)

Laboratoire de Simulation et Modélisation


State of the art: Understanding and modeling vertical leakage currents and trapping effects in GaN substrates on Si are among the crucial subjects of studies aimed at improving the properties of GaN power components : current collapse and Vth instabilities reductions, reduction of the leakage current in the OFF state. Many universities [Longobardi et al. ISPSD 2017 / Uren et al. IEEE TED 2018 / Lu et al. IEEE TED 2018] and industrials [Moens et al. ISPSD 2017] are trying to model vertical leakages but until now, no clear mechanism has emerged from this work to model them correctly over the entire range of voltage and temperatures targeted. In addition, modeling the effects of traps in the epitaxy is necessary for the establishment of a a robust and predictive TCAD model of device. For LETI, the strategic interest of such a work is twofold: 1) Understanding and reducing the effects of traps in the epitaxy impacting the functioning of GaN devices on Si (current collapse, Vth instabilities?) 2) Reaching the leakage specifications @ 650V necessary for industrial applications. The candidate will have to take charge in parallel of the electrical characterizations and the development of TCAD models: A) Advanced electrical characterizations (I (V), I (t), substrate ramping, C (V)) as a function of temperature and illumination on epitaxial substrates or directly on finite components (HEMT, Diodes, TLM ) B) Establishment of a robust TCAD model integrating the different layers of the epitaxy in order to understand the effects of device instabilities (dynamic Vth, dynamic Ron, BTI) C) Modeling of vertical conduction in epitaxy with the aim of reducing leakage currents at 650V Finally, the candidate must be proactive in improving the different parts of the substrate

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3D modeling and characterizations to improve performances, durability and robustness of Solid Oxide Electrolysis Cells

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire Production d'Hydrogène



Solutions avancées pour l'hydrogène et les piles à combustible pour la transition énergétique (.pdf)

The SOCs lifetime is still insufficient to envisage the industrial deployment of this technology. Therefore, their performances and durability still need to be improved. Multiscale physically-based models are especially relevant to understand and decorrelate the intertwined mechanisms involved in the electrodes response. The post-doc will develop a 3D electrochemical model that will be coupled with an existing multiscale numerical tool already available at the laboratory. In collaboration with two PhD students, the degradation phenomena such as the electrode microstructural evolutions will be implemented in the modeling tool and then validated using advanced material characterizations methods (e.g. 3D electrode reconstructions performed at the European Synchrotron Radiation Facility - ESRF). Once validated, the model will be used to assess the impact of the degradation phenomena on the cell performances. Simulations will be also carried out to identify optimized electrodes that will be proposed for manufacturing at CEA.

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Numerical Meta-modelization based study of the propagation of ultrasonic waves in piping system with corroded area

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Acoustique



Usine du futur dont robotique et contrôle non destructif (.pdf)

The aim of the ANR project PYRAMID ( is to develop some technics of detection and quantification of the wall thinning due to flow accelerated corrosion in piping system. In the framework of this project involving French and Japanese laboratories, CEA LIST develops new numerical tools based on finite elements dedicated to the modelling of an ultrasonic guided wave diffracted by the corrosion in an elbow pipe. These solutions support the design of an inspection process based on electromagnetic-acoustic transduction (EMAT). To this end, the ability of CEA LIST to adapt meta-modeling tools of its physical models will be the key asset to allow intensive use of the simulation.

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Combinatorial optimization of base materials for the design of new materials

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Intelligence Artificielle et Apprentissage Automatique



Data intelligence dont Intelligence Artificielle (.pdf)

The design of new materials is a field of growing interest, especially with the emergence of additive manufacturing processes, thin film deposition, etc. In order to create new materials to target properties of interest for an application area, it is often necessary to mix several raw materials. A physicochemical modeling of the reactions that occur during this mixing is often very difficult to obtain, especially when the number of raw materials increases. We want to free ourselves as much as possible from this modeling. From experimental data and business knowledge, the goal of this project is to create a symbolic AI capable of groping for the optimal mixture to achieve one or more given properties. The idea is to adapt existing methods of operations research, such as combinatorial optimization, in a context of imprecise knowledge. We will focus on different use cases such as electric batteries, solvents for photovoltaic cells and anti-corrosion materials. Within the project, you will: ? Study the state of the art, ? Propose one or several algorithms to prototype, and their evaluation, ? Disseminate the resulting innovations to the consortium and the scientific community, through presentations, contributions to technical reports and / or scientific publications. Maximum duration: 18-24 months (regarding your experience).

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