Compressive sensing and signal processing of sparse acoustic signals for sources detection, classification and localization
Offer N°: SL-DRT-15-0645
The objective of this PhD thesis is the monitoring of industrial electric systems. In particular, the focus is put on rare events detection with important failures on the system (as electric arcs) from a distributed acoustic sensors network. This PhD deals with the optimization of the following steps: signal digitalization, signal processing and data transmission. Those subjects lie in the context of compressive sensing and signal processing of compressed data.
Fabrication and testing of multijunction IIIV on Si solar cells
Offer N°: SL-DRT-15-0714
The crystal Silicon photovoltaic industry recently demonstrated record efficiency values over 25%, approaching the Si theoretical limit. Multijunction solar cells based on IIIV materials can overcome this limit: efficiencies over 44% have been reported on 4 junctions under 247suns. Due to the elevated cost of these cells, this technology is dedicated to high concentration.
An intermediate solution consists in combining IIIV and Si materials in order to:
MEMS inertial sensors design and fabrication based on an innovative process
Offer N°: SL-DRT-15-0727
The inertial MEMS sensors market, principally driven by Smartphones, automotive and connected objects, is huge and is still growing. This results in a high pressure on price, which means a global race focusing onto miniaturization.
A new architecture and its manufacturing process, patented by CEA-Léti, opens the way for a new generation of sensors whose surface could be divided by three, for the same performance.
Study of the intra-layer effect, diffusion effect, and shot noise for advanced lithographie
Offer N°: SL-DRT-15-0712
CEA-LETI is leading a program for enabling the Multi-beam lithography technology with low energy. In the frame of this industrial consortium, CEA-LETI is proposing a PhD to deeper understand the pattern roughness for Multi-beam lithography. In the first step the candidates will have to characterize the roughness of the pattern with Scanning Electron Microscope (SEM) and 3D Atomic Force Microscope.