News : Technological challenges
January 01 2023
Impact of mechanical properties of thin layers in SmartCut technology.
The SmartCut technology is nowdays widely used on the manufacture of innovative substrates such as SOI (Silicon-on-Insulator). The fundamental physical phenomena underlying this process are still being actively studied. In particular, the catastrophic fracture step, which allows the transfer of a very thin layer of a donor substrate onto a base or receiver substrate. An […] >>
January 01 2023
Optical coupling between high power lasers and photonic circuits for LiDAR applications
Silicon photonics was initially developed for high-performance telecommunications, but new applications quickly emerged. These include the development of LiDARs (Light Detection And Ranging) intended for environmental imaging for vehicles and autonomous systems. Indeed, existing LiDARs are made of discrete components whose mechanical assembly makes them fragile, expensive and bulky. By using a photonic chip, the […] >>
January 01 2023
Development of nanodielectrics for power electronics
Power electronics used in particular for the electric vehicle requires the fabrication of smaller and smaller devices able of sustaining high currents and high working voltages. The miniaturization of these components requires the development of new dielectric materials with high breakdown field. A promising approach is to combine the high dielectric constant of inorganic oxides […] >>
January 01 2023
Study of the anisotropy of dopant and compositional gradients in the reference ternary alloy for infrared detection
This thesis concerns the field of HgCdTe infrared detectors for astrophysical applications, for which the Infrared Laboratory of CEA -Leti is a world leader. The student will join the infrared laboratory which includes the entire detector manufacturing process. He will produce the samples using the technological means of elaboration available in the Leti clean room. […] >>
January 01 2023
Networks of stochastic magnetoresistive components for ultra-low-power cognitive computing
The automated resolution of cognitive tasks primarily relies on learning algorithms applied to neural networks which, when executed on standard architectures, lead to a power consumption several orders of magnitude larger than what the brain would require. This consumption can be drastically reduced by using hardware computing systems with an architecture inspired by biological or […] >>