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.
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.
Electric field and ab initio simulations, application to RRAM
Since several years, LETI/DCOS is engaged in a simulation effort of microscopic phenomena at the heart of oxide-based RRAM operation (made of HfO2, Ta2O5, Al2O3). The correct description of an external electric field applied to a MIM device (Metal-Insulator-Metal) is now possible thanks to two methods one by an orbital separation approach [1] the other by using the non equilibrium green function formalism [2]. In this work, we propose to develop and to handle these methods by combining already existing simulation approaches. The main goal is to study the degradation mechanisms of an oxide by following the oxygen atoms movements coupled directly to the applied external electric field. These mechanisms are not known and this study will support the optimization and characterization efforts already engaged at LETI on RRAM functional prototypes. The targeted simulations tools are SIESTA for the DFT part, and TB_SIM for the electronic transport part.
[1] S. Kasamatsu et al., « First principle calculation of charged capacitors under open-circuit using the orbital separation approach, PRB 92, 115124 (2015)
[2] M. Brandbyge et al., « Density functional method for nonequilibrium electron transport », PRB 65, 165401 (2002)
Design and implementation of force feedback by electrical sense for remote operation with submarines or aerial robots
Since few years, the Bio-inspired Robotics group of the IRCCyN Robotics team is developing a bio-inspired perception mode found on some freshwater tropical fish: the electrical sense. This active sense is based on the distortions measures, due to environment, of an electric field produced by the fish. Based on this principle Irccyn developed in the context of a European project called Angels, the first autonomous underwater robot capable of moving by means of the electrical sense . In the future, CEA TECH and Irccyn want to extend this first result in multiple directions, including the remote operation of submarines manipulators and aerial robots domains. The force feedback should be emulated by the use of the electrical sense. Integrated in the Bio-inspired robotics team of IRCCYN , post -doctoral fellow will contribute to the development of the electric sense and its use for underwater and aerial teleoperation . He will participate in the design and development of new sensors inspired by electric fish and their use for underwater telerobotics. The results of its work will underpin the industrial demonstrator system (teleoperation offshore) to be developed under the project CEA TECH / IRCCYN Bio-inspired robotics.
Eco-innovation of insulating materials by AI, for the design of a future cable that is long-lasting, resilient, bio-sourced and recyclable.
This topic is part of a larger upcoming project for the AI-powered creation of a new electrical cable for future nuclear power plants. The goal is to design cables with a much longer lifetime than existing cables in an eco-innovative approach.
The focus is on the cable insulation because it is the most critical component for the application and the most sensitive to aging. The current solution is based on adding additives (anti-rad and antioxidants) to the insulation to limit the effects of irradiation and delay aging as much as possible. However, there is another solution that has never been tested before: self-repairing materials.
The project to which this topic is attached aims to design and manufacture several test model of insulation specimens. With several test characterization protocols, in order to verify the gain in terms of reliability and resilience. The results obtained will begin to fill a future database for the AI platform Expressif, developed at CEA List, which will be used to design the future cable.
Aging study of silicon nanowires used as piezoresistive detection gauges for achieving inertial MEMS sensors.
Today’s sensors are present in all areas: housing, automotive... Thanks to recent developments in microelectronics, new generations of sensors combine high performance, small size and low cost. In this context, CEA-LETI has proposed an innovative concept called M&NEMS for the realization of inertial sensors such as accelerometers, magnetometers and gyroscopes. The M&Nems concept combines MEMS and NEMS to take advantage of the great inertia generated by a MEMS mass and the high detection sensitivity of piezoresistive silicon nanowires. Demonstrators have already been carried out and have shown the good potential of the M&Nems concept. One of the main challenges which remain to overcome is the reliability of sensors based on this concept and specifically the reliability of the piezoresistive nanowires. The research work will be mainly focused on the study of the failure modes of these piezoresistive nanowires gauges i.e. the identification of physical phenomena and the development of failure models. In order to do this study, a first preliminary work will be focused on the physical mechanisms which manage the electrical conduction in the nanowires: piezoresistivity, charge trapping, relaxation field effect ... The work will then continue by the study of the failure modes of nanowires, the goal will be to understand and model the physical aging of these nanowires: it will be possible to rely on the knowledge of the physics of nanowires conduction but also play with the physical parameters of these nanowires such as silicon doping, the process fabrication, the packaging technique, the thermomechanical stresses, the scale effect due to surface / volume ratio, or the surface condition. Finally, models of aging will allow proposing and validating technological choices to ensure the nanowires lifetime depending on operating conditions of the sensors.