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

PhD : selection by topics

High-throughput Computation of Defects in Semiconductors

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire Composants Récupération de l'Energie

01-01-2018

SL-DRT-18-0206

Ambroise.VANROEKEGHEM@cea.fr

We propose a large-scale computational screening of defects in semiconductors. The project will be the first to inter-relate growth conditions, defect types and concentrations, and functional transport properties. The study will combine several approaches developed by the two project partners and streamline them into an automated flow linked to a material-defects database. Innovative machine learning classification and regression algorithms will be applied to unveil hidden trends and relationships and to accelerate the screening process. A novel understanding of semiconductors according to their prevalent defect types will thus emerge. Specific focus will be put on the lattice thermal conductivity to elucidate the characteristic relationships between the defects and the materials functional properties. In addition to its fundamental interest, this project will impact many industrial technologies where semiconductor defects are key to their performance.

Bio-inspired vision chain for scene analysis.

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Adéquation Algorithmes Architecture

01-10-2018

SL-DRT-18-0309

laurent.soulier@cea.fr

Artificial vision systems (camera(s) and processor(s)) have recognition capabilities well below those achieved by biological systems (eye - cortex). Moreover, biological systems are able to process information within a few milliseconds, which is still out of range of electronic systems, even though their electronic image sensors are far from achieving the resolution of human eyes (few dozen megapixel against more than one hundred million). This thesis aims at addressing the challenge posed by the biology by designing integrated bio-inspired sensor architectures. Our approach is based three assumptions: first, resolution biological imaging sensors is not uniform, the best resolved zone (the fovea) is dedicated to the acquisition of the areas of interest of the scene; secondly, pre-processing from the sensor are used to compress the information; finally, the processing of information is context and prior knowledge dependent. This exploratory thesis, aims to devise, within the frame of these hypothesis, breakthrough solutions with respect to the state of the art, to endow autonomous artificial systems (drones of all kinds (UAV, UGV, ...), machine tools, smart camera) of ability to perception of their complex environment, while using only limited resources, i.e. those of embedded systems. The candidate should have strong tastes or skills in image processing and digital architectures.

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