Multiscale Modelling of Radiation Induced Segregation
Irradiation produces in materials excess vacancies and self-interstials that eliminate by mutual recombination or by annihilation at sinks (surfaces, grain boudaries, dislocations).
It sustains permanent fluxes of point defects towards those sinks. In case of preferential transport of one componant of an alloy, the chemical composition is modified in the vicinity of the sinks: a Radiation Induced Segregation (RIS). Its modelling requires a good description of the alloy properties: its driving forces (derived from the thermodynamics) and its kinetic coefficients (the Onsager matrix). The objectif on this project is to combine (i) atomic models (Kinetic Monte Carlo simulations and Self-Consistent Mean Field), fitted on ab initio calculations, that provide the Onsager coeffcients and the driving forces and (ii) a Phase-Field modelling that will give a description of the evolution of the alloy under irradiation at much larger time- and space-scales. The approach will be applied to Fe-Cr and Fe-Cu alloys, already modelled at the atomic scale. RIS will be first modelled near grain boundaries, then near dislocation loops. Special attention will be paid to the effect of elastic stresses on the RIS.
Ge-on-Insulator (GeOI) substrates for photonics
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.
Implementation of a software package for the simulation of the Infrared Thermography Non Destructive Testing method
The CEA LIST implements simulation tools for several Non Destructively Testing (NDT) techniques, integrated to the CIVA software platform. The different methods used, nowadays in the CIVA platform, concern the ultrasonics, eddy current and radiography techniques. The TREFLE is a reference lab in thermics and had developped some original modelling approachs for the control by Infrared Thermography (IR) method. In the frame of a project funded by the Aquitaine region, these two labs collabore to implement simulation tools for the NDT by the Infrared Thermography technique, dedicated to industrial applications and accesssible to a non-numericians public.
The objective of this post-doc position is the implementation of physical modelling (in a matlab environment) for the resolution of transient thermal problems in multilayers configurations (like composite materials used in aeronautics), eventually anisotropic, for a flash or a periodic excitation with uniform or point irradtiation.
Optimal management of a tertiary energy system
In the solution concerning residential or tertiary sites that consume and produce electrical energy , the objective is to optimize the use of energy based on economic criteria or constraints networks (adaptation of the consumption) without introducing perturbations of user comfort. The purpose of this position is to develop a solution for "optimal management of the use of solar energy in a tertiary building integrating EV charging stations and storage." according to three objectives:
- Minimize the cost of consumption based on a dynamic tarif - Maximize the use of solar energy - Minimize the power demand of the network. Taking into account the LCOS (levelised Cost Of Storage) of battery . The Post- Doc will contribute and participate in: - Specification of tertiary system - Development of algorithms for managing a tertiary system - Deploy and test the proposed solution.
Contribution to the development of miniature antennas measuring devices
The generalization of RF links operating at VUHFfrequencies to equip an increasing number of communicating electronic devices helps to intensify research on miniaturization and integration of antennas. As a result, significant progress are regularly carried out to reduce the size of antennas and it is not uncommon to find work describing antenna structures of 1/30 of the wavelength maximum dimension. Increased sensitivity to the operating environment is observable with electrically small antennas. This feature is reflected by problems of measurement of electrical and radiation properties that may be altered with the standard techniques of connecting a measuring cable to the antenna. Accordingly, the subject seeks to develop techniques for electrically small antennas charterization using non-invasive methods, that is to say does not interfere (or few) under test antenna. Two techniques will be investigated based on the work already done in the laboratory. The first technique is based on the far field electromagnetic reflectometry. The second technique involves the use of an RF-optical transducer in the vicinity of the antenna under test for a particular design of miniature optic RF conversion reflectometer for measuring antenna impedance.
3D occupancy grid analysis with a deep learning approach
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.
Construction of databases for radionuclide identification based on neural networks (NANTISTA project)
The project NANTISTA (Neuromorphic Architecture for Nuclear Threat Identification for SecuriTy Applications) deals with the prevention of illegal traffic of nuclear materials at international borders. The project aims at the development of a detection platform using plastic scintillators for fast radionuclide identification (such as fissile materials) based on neural networks. The post-doctoral subject consists in the development of the detection system and the construction of databases dedicated to the learning process and the optimization of the neural networks. The databases will be built with experimental measurements given by radioactive sources. Radiation-matter simulations (Monte-Carlo codes Geant4 and Penelope) will also be implemented for the construction of the databases.
Design and implementation of a bio-inspired sense, application to offshore teleoperation and to operator assistance
In recent years, the Bio-inspired Robotics Group of Robotics team IRCCyN has developed an artificial bio-inspired electric fish sense. To emulate the electrical sense, resistive probes were used for piloting the IRCCyN submarine autonomous robot.
For its part, within the Interactive Robotics Laboratory (LRI), the CEA LIST has been pursuing for several years a research activity in the field of force feedback telerobotics. The operator manipulates a slave robot located in hostile environments via a master arm located in a safe area and a computer system.
The candidate’s work will take place in a CEA- IRCCyN project running in parallel over a first project whose purpose is to demonstrate the concept of electro- haptic loop on a Cartesian arm carrying an electric probe with a fixed and known geometry. The postdoc will be in charge of implementing the loop on a "marinized" manipulator arm with a complex geometry. To do this, with the assistance of CEA and IRCCyN , he/she will support the preparation of this arm and adaptation of electrical sensor (emitter electrodes , receiver , electronic) architecture considered , as well as the adaptation of the monitoring / control of the haptic interface at the base of the electro-haptic loop. In addition to the technological challenges of this adaptation, the candidate must also consider different strategies to exploit the electric field on a multi-body system of variable geometry.
Experimental validation and proof of concept of this new offshore teleoperation system will be carried out on scenarios, to be defined, representative of the final application.
Investigation of the reliability of Resistive RAMs for high density memories application
In this postdoc, we propose to investigate Resistive memories (RRAM) as a Storage Class Memory (SCM) for high density memory applications. To this aim, both CBRAM and OXRAM will be studied and compared. RRAM technologies, integrating various resistive layers, top and bottom electrodes will be integrated.
Then electrical characterization will be performed on these different memory options. The impact of the integration flow on the memory characteristics will be addressed, to evaluate how critical integration steps may impact the memory operation. In particular, MESA (the RRAM stack is etched) vs Damascene (the RRAM stack is deposited in a cavity) approaches will be compared.
After the evaluation of the memory basic operation (forming, SET and RESET operation speed, required voltages…), a specific focus will be made on reliability. In particular, endurance will be deeply investigated and optimized. The impact of SET RESET conditions (including smart programming schemes) on the window margin and number of cycles will be analyzed. Finally, the variability issue will be highly covered, in order to quantify how cycle to cycle and device to device variability close the window margin of the RRAM. Specific reliability concerns (read noise…) will also be addressed. Extrapolations on the maximum density a given RRAM technology can reach will be drawn.
Based on this detailed study, a benchmark of all the tested RRAM technologies will be made, to identify the pros and cons of each option, and highlight the tradeoff that have to be found (among them: memory speed, endurance, operating voltages, consumption…).
Distributed optimal planning of energy resources. Application to district heating
Heating district networks in France fed more than one million homes and deliver a quantity of heat equal to about 5% of the heat consumed by the residential and tertiary sector. Therefore, they represent a significant potential for the massive introduction of renewable and recovery energy. However, heating networks are complex systems that must manage large numbers of consumers and producers of energy, and that are distributed in extended and highly branched geographical zones. The aim of the STRATEGE project, realized in collaboration among the CEA-LIST and the CEA-LITEN, is to implement an optimal and dynamic management of heating networks. We propose a multidisciplinary approach, by integrating the advanced network management using Multi-Agent Systems (MAS) and by considering simplified physical models of transport and recovery of heat developed on Modelica.
The post-doc’s goal is to design mechanisms of planning and optimization for allocation of heat resources that consider the geographical information from a GIS and the predictions of consumption, production and losses calculated with the physical models. In this way, several characteristics of the network will be considered: the continuous and dynamic aspect of the resource; sources with different behaviors, capabilities and production costs; the dependence of consumption/production to external aspects (weather, energy price); the internal characteristics of the network (losses, storage capacity). The developed algorithms will be implemented in a existing MAS management plateform and will constitute the main brick of a decision-support engine for the management of heating systems. It will initially operate in a simulated environment and in a second time online on a real system.