Internship
(in french) ELABORATION et CARACTERISATION de couches minces de Ge/Si épitaxiées sur substrat de Silicium
Starting from January 2021
Internship
Ce stage qui s’inscrit dans une collaboration entre le C2N (centre de Nanosciences et de Nanotechnologies) et l’ IRFU (Institut de recherche sur les lois Fondamentales de l’univers), fait partie d’un projet interne du CEA intitulé : Nanostructures Si/Ge/Si pour détecteurs pixels.
Ces détecteurs pixels permettent de reconstruire les trajectoires des particules chargées au plus près du point de collision. Le projet DOTPIX (Development Of a the Technology needed for a large micrometric resolution PIXel position sensitive detector) consiste donc à développer ces détecteurs sensibles à la position des pixels de grande résolution micrométrique puis de les caractériser en vue de leur utilisation sur des expériences de physique des particules auprès de futurs collisionneurs e+e-.
Le stage intervient dans l’étape de développement de la technologie Ge On Si nécessaire au fonctionnement du dispositif DOTPiX. Le/la candidat(e) effectuera ce stage au sein de l’équipe Seeds du C2N qui a une longue expérience sur la croissance par épitaxie de matériaux désaccordés. Il/elle sera en charge de l’épitaxie de couches d’épaisseur nanométrique de Si, SiGe et Ge sur des substrats de Si (texturé ou non). La technique de croissance cristalline utilisée est l’UHV-CVD (Ultra High Vacuum Chemical Vapor Deposition) et le travail s’effectuera principalement dans la salle blanche du C2N. Il/elle travaillera aussi aux caractérisations in et ex-situ de ces couches avec les moyens disponibles au C2N (RHEED, MEB, AFM, XPS, ellipsométrie, caractérisations électriques). Il/elle sera également amener à collaborer avec d’autres laboratoires comme le GEMAC (Groupe d'Étude de la Matière Condensée) pour les analyses SIMS et l’IRFU pour des caractérisations électriques complémentaire et des simulations numériques si nécessaire.
Connaissances requises
Compétences opérationnelles
Laboratoire et équipe d'accueil
C2N, CNRS / Université Paris Saclay, 10 Boulevard Thomas Gobert, 91120 Palaiseau FRANCE Département Matériaux / Equipe SEEDs
Contact: Géraldine Hallais
Téléphone : 01 70 27 03 49 / E-mail geraldine.hallais @c2n.upsaclay.fr
Dates : janvier à septembre 2021
Artificial intelligence models for opto-mechanical silicon metamaterials
Starting from January 2021
Internship
Stimulated Brillouin scattering (SBS), mediating (THz) photons and (GHz) acoustic phonons, has an immense potential for opto-acoustic signal processing which have no analogue in conventional electro-optic or all-optic approaches. Namely, SBS has shown ultra-high-resolution filtering and remarkable low noise signal regeneration. Furthermore, the narrow linewidth and low phase noise of Brillouin lasers, make them an ideal solution for high-performance micro-wave signal generation.
SBS has been extensively developed in optical fibers, however the phonon leakage towards the cladding in conventional silicon-on-insulator (SOI) waveguides precluded the observation of SBS in Si photonics. However, the recent development of a new generation of Si optomechanic waveguides (see Fig. 1) has removed this barrier, revolutionizing the field and allowing the experimental demonstration of SBS nonlinearities surpassing Kerr and Raman effects (in 2013 [1]), complete phononic bandgap (in 2014 [2]), net amplification (in 2016 [3]), and Si Brillouin laser (in 2017 [4]). Still, fully exploiting the potential of these Brillouin optomechanical interactions in silicon requires of novel design strategies and, and advanced design tools allowing their optimization.
The goal of this internship is to develop artificial intelligence models to harness the unique degrees of freedom of subwavelength Si nanostructures to independently tailor photonic and phononic modes, providing simultaneous tight confinement and strong overlap, thus maximizing the efficiency of the SBS effect.
followed PhD
Deep learning models for miniaturized silicon photonics sensors
Starting from January 2021
Internship
Lab-on-chip sensors are miniaturized circuits that integrate all key functionalities within a single chip, including target preparation, detection and data analysis. Ideally, these devices should be compact, robust and low cost, allowing large-volume production. This would allow the widespread deployment of a wide range of high-impact applications such as invasive medical diagnostics, food quality control and air pollution monitoring. In addition, lab-on-chip sensors should comprise sophisticated and intelligence enough (data monitoring, processing and analysis) to be used by non-skilled personal.
Deep learning methods rely on a training process and different abstraction levels to yield models able to automatically discover the representations needed for detection or classification. Deep learning models can perform precise detection regardless the changes in operation parameters within a training range, thus obviating the need for tight calibration processes. The different abstraction levels are learned from data training, and not from engineering design. This provides deep learning methods with a unique flexibility and outstanding processing capabilities that are already being exploited in image or speech recognition or predicting DNA mutations. We experimentally demonstrate for the first time that the use of machine learning substantially improves the tolerances of on-chip SHFT spectrometers against temperature variations, thus opening a new route for their use in realistic applications outside the controlled laboratory environment.
The goal of this internship will be to explore the use of deep learning algorithms to improve the performance, robustness and flexibility of miniaturized silicon photonics sensors. Simplified models of Si sensors and deep learning algorithms will be combined to alleviate performance degradations due to non-idealities and to develop advanced detection and processing functionalities.
