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

PostDocs : selection by topics

Innovative modeling for technology-design-system co-optimization

Département Composants Silicium (LETI)

Laboratoire de Simulation et Modélisation



The post-DOC will support the device modeling part of a research project investigating new methodologies for system and circuit optimization with the aim of achieving a better integration between the knowledge of the detailed characteristics of a specific technology, the circuit-design methodology and the system architecture. The practical goal is to leverage the existing multi-disciplinary know-how for benchmarking of system and technologies to advance the analysis past the usual PPA, PPAY and PPAC approaches that are commonly deployed in such cases. In more detail, the post-DOC will develop "pre"-spice models for actives and passives which will constitute the basic bricks for the optimization methodology developed in the overall project. Active device modeling will have a starting point in the works of EPFL based on the analytical expression of invariants such has the inversion coefficient.

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Detection of small particules in the environment with nanomechanical resonators

Département Composants Silicium (LETI)

Laboratoire Composants Micro-Capteurs



Today, there are solutions for detecting and quantifying PM10 and PM2.5 type particles (10 and 2.5µm diameter); their reliability depends essentially on their cost. These solutions are essentially optical, and they must be improved for particles down to a micron. For even smaller particles that are even more dangerous to health, there does not seem to be an obvious solution today. Nanomechanical resonators perform very well in these size / mass ranges, as demonstrated by our recent results obtained with our system for biological objects in liquid, recently published by the journal Science ( 362/6417/918). These nanoresonators therefore appear as a promising technology for the detection of PM especially for certain applications of air quality control in real time. It will therefore be necessary to study the possibility of detecting particles in the air, in particular those which are hardly detectable today (PM <0.5). We will rely very largely on the systems developed for the detection of biological particles in liquid medium. It will be a question of taking advantage of this know-how and of adapting the system architectures, but also the nanomechanical resonators themselves for the detection of particles in aerosol. We will target representative nanoparticles, organic, pathogenic or non-pathogenic. In terms of resonators, we will also take advantage of current fabrications, with specific designs (electrical or optomechanical) for this application. We will particularly study the possibility of preventing fouling problems. The candidate will be fully integrated into the team around mass detection with nanoresonators.

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Sizing and control optimisation of a hydrogen production system coupled with an offshore wind farm





Coupling MRE (Marine Renewable Energy) and hydrogen sectors reveal an important potential long-term assets. The MHyWind project suggests to estimate the energetic and economic potential of a hydrogen production system integrated into a substation of an offshore wind farm. The hydrogen produced and stored locally will be distributed by boat for harbour uses, as a replacement of fossil fuels. For that purpose, it will be organized a simulation which will integrate all the energy chain towards the harbour uses of hydrogen. It will allow to estimate various configurations and sizing according to the local uses, valuation leverages, control modes and behavior of the system. The criteria will be the producible (kg of H2 producted and used) and complet costs (CAPEX and OPEX). The objective of the postdoctoral student will be to develop the simulation tool on this applicative being fully integrated with the teams of concerned laboratories.

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Simultaneous Localisation and Mapping with an RGB-D camera based on a direct and sparse method

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Vision & Ingénierie des Contenus (SAC)



Recent advances in the methods of locating a device (smartphone, robot) in relation to its environment make it possible to consider the deployment of augmented reality solutions and autonomous robots. The interest of RGB-D cameras in such a context is notable since it allows to directly acquire the depth map of the perceived scene. The objective of this post docorate consists in developping a new SLAM (Simultaneous Localisation and Mapping) method relying on a depth sensor. To reach a solution both robust, accurate and with small CPU/memory comsumption, the depth image will be exploited though a direct and sparse approach. The resulting solution will be then combined with the solution of "RGB SLAM Constrained to a CAD model" developped in our laboratory, resulting finaly in an "RGB-D SLAM Constrained to a CAD model"

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3D occupancy grid analysis with a deep learning approach

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Infrastructure et Ateliers Logiciels pour Puces



The context of this subject is the development of autonomous vehicles / drones / robots. The vehicle environment is represented by a 3D occupancy grid, in which each cell contains the probability of presence of an object. This grid is refreshed over time, thanks to sensor data (Lidar, Radar, Camera). Higher-level algorithms, like path planning or collision avoidance, think in terms of objects described by their path, speed, and nature. It is thus mandatory to get these objects from individual grid cells, with clustering, classification, and tracking. Many previous publications on this topic comes from the context of vision processing, many of them using deep learning. They show a big computational complexity, and do not benefit from occupancy grids specific characteristics (lack of textures, a priori knowledge of areas of interest?). We want to explore new techniques, tailored to occupation grids, and more compatible with embedded and low cost implementation. The objective of the subject is to determine, from a series of 3D occupation grids, the number and the nature of the different objects, their position and velocity vector, exploiting the recent advances of deep learning on unstrucured 3D data.

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Ge-on-Insulator (GeOI) substrates for photonics

Département Composants Silicium (LETI)

Laboratoire Intégration et Transfert de Film



The induction of tensile strain in intrinsic and doped Germanium (Ge) is one approach currently explored to transform the Ge indirect bandgap into a direct one. To take full advantage of Ge, we study the Ge CMOS photonics platform with Ge-on-Insulator (GeOI) structure, which enables strong 2D optical confinement in the Ge photonic-wire devices. One recent study in our lab showed the interest of a method of incorporation of mechanical stress into Ge, one of the essential ingredients of the laser. In particular, the method could be applied to the massive Ge, making compatible gap direct and crystalline quality. Post-doc objectives : Development of GeOI substrates from massive Ge donors with tensile strain inside the Ge film. These developments will be realized from the existing Smart Cut / thinning processes, combined with technological steps to overcome their current limits (SAB bonding). The substrates obtained will be characterized to determine their state of deformation as well as their damage (Raman / XRD) and final GeOI substrates will be provided to the application laboratories for the production of photonic components.

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