Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Li/S batteries with sulfide solid electrolyte

Département de l'Electricité et de l'Hydrogène pour les Transports (LITEN)

Laboratoire Matériaux



To further improve the performance of lithium batteries, all-solid design has been proposed employing inorganic or polymeric solid electrolytes. Best room-temperature ionic conductivity is demonstrated by sulfides approaching the values typical for liquid electrolytes. This family of ionic conductors is also interesting regarding the low synthesis temperature (compared to that of oxides) and facility of implementation by cold pressing (due to soft particles). Current limitations for sulfide solid electrolytes arise from their electrochemical reactivity with electrodes. Thus, glass-ceramic sulfides of composition Li2S-P2S5, passivate the surface of metallic Li and decompose in contact with charged cathode at high potential. The PhD project aims at evaluation of compatibility of sulfide solid electrolyte with low-potential cathode, such as elemental sulfur. This active material works at potentials as low as 2V but, in return, it has a high capacity of 1675mAh/g. To benefit from high capacity and low voltage, the best candidate for counter electrode is metallic Li. Accordingly, Li/S system with liquid electrolyte has been studied in LITEN for 10 years. The developed techniques allowed to produce cells with encouraging performances. However, the presence of liquid electrolyte leads to limitations on cycle-life of Li counter electrode. Accordingly, the replacement of liquid electrolyte by solid conductors can be a solution for this low-voltage system. Based on our previous experience in Li/S systems and sulfide solid electrolyte, the PhD project will place the first bricks into the Li/sulfide/S solid system. In particular, one of the targets will be to develop the cathode formulation adapted to volume and morphology change of all-solid composite cathode during cycling. Another objective of this PhD project will deal with optimization of interface between lithium and solid electrolyte. In this task coating as well as passivation solutions will be considered. In the end of PhD study, a proof of concept will be presented as a functional pouch cell. The improvement of security due to solid electrolyte will be evaluated in cooperation with a PhD project on characterization proposed by STB/SAMA.

Machine learning based simulation of realistic signals for an enhanced automatic diagnostic in non-destructive testing applications

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Electro-magnétisme



Model based solutions for automatic diagnostic in the field on non-destructive testing are currently a topic of great interest in both academic and industrial communities. Their ultimate objective is to provide a qualitative or quantitative evaluation of the inspected material state (sound, flawed, flawed with anomaly dimensions or criticality) in an industrial context like a production line. Such tools, providing inputs for real-time process control, contribute to the general trend in Europe that aims at modernizing Industry and services [1]. The CEA LIST institute is an internationally recognized research institution in the field of nondestructive testing. It develops the CIVA software [2], which offers multi-physics models and is considered as a leading product for simulation for NDT applications. Accurate models able to reproduce experimental signals prove very helpful in an inversion process aiming at classifying or characterizing flaws [3]. However, as they do not account for disturbances and parameters variability occurring during an experimental acquisition, simulated signals inherently look ?perfect? and are, for instance, easily distinguishable from experimental data. This PhD subject aims at improving the match between simulation and experimental data, by augmenting the simulation with another contribution on can generally refer to as ?noise?. The strategy proposed to obtain such noise contribution is to apply machine-learning techniques like dictionary learning to a set of representative experimental data. Alternatively, a deep learning model can be trained to analyze real data and then distinguish between contents (flaw signals) and style (the rest, which is not simulated by physical models). Afterwards, the augmented simulation tool will be able to reproduce closely experimental data, take into account specific discrepancies due to a particular environment and reproduce the variability observed experimentally. It will thus enhance the performance of model based tools developed at CEA LIST for sensibility analysis, management of uncertainty and diagnostic. REFERENCES [1] [2] [3] M. Salucci et al., "Real-Time NDT-NDE Through an Innovative Adaptive Partial Least Squares SVR Inversion Approach," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 11, pp. 6818-6832, Nov. 2016

Cyber-reasoning systems to the next level: bringing learning and deduction together

Département Ingénierie Logiciels et Systèmes (LIST)

Laboratoire pour la Sûreté du Logiciel



This PhD aims at understanding how deductive methods based on automatic reasoning (used in program analysis) can be combined with inductive methods based on machine learning (developed in Artificial Intelligence) in order to obtain new automatic methods for program analysis that could infer program behaviours in a precise and sound way. Then, these new methods will be applied on code security problems such as retro-engineering or code strengthening. We will focus on symbolic learning (constraint learning) and deductive methods based on symbolic execution and SMT solvers.

The Backend Selector: from material development to device performance

Département Composants Silicium (LETI)

Laboratoire de Composants Mémoires



The maturity of non-volatile resistive memory technologies NVRM (such as phase-change memory PCM) for both Storage Class Memory (SCM) and embedded applications has demonstrated in recent years the need for the development of a reliable backend selector device to replace transistor selection. This technology allows the stacking of multiple levels of memory in 3D, in a so-called "Crossbar" architecture, increasing the storage density while taking advantage of the extraordinary performances of NVRM devices. LETI is today at the state of the art regarding the development of materials for integration into backend selector devices, especially for Ovonic Threshold Switching selectors (OTS). In the framework of this PhD new materials will be explored to meet the required specifications in terms of endurance, temperature stability, threshold voltage and scalability capability becoming more and more stringent. For this, the understanding of the physics and of the phenomena related to the functionality of these devices becomes fundamental. In addition, innovative memory+selector co-integration architectures will be investigated to finally achieve the integration of these solutions in an advanced Crossbar demonstrator. The candidate should preferably have a very good level of knowledge in semiconductor physics and materials science. The candidate will be in contact with experts from different fields because of the multidisciplinary nature of the work (materials, integration, electrical and physicochemical characterization, and modeling). In addition, good team spirit and a good English language proficiency is required.

Stretchability : which impact on printed electronics ?

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire Composants Optiques Imprimés



Stretchability is a main challenge that printed electronics has to face in order to integrate printed functions (sensors, actuators, energy harvesters) inside systemes. This integration close to the use imposes strong constraints on device specification : stretchable, wearable, co-integration with elements having a different mechanical behavior (rigid/stretchable). This Phd work aims at studing the impact of this new constraints on technologies printed on the PICTIC Plateform of CEA Grenoble. The study will first focus on building blocks of printed electronics (conductive track, dielectric, capacity, organic semi-conductor). Then it will be applied on few devices already under control in the lab on usual plastic substrate. The study will figure out the materials blocking points and the innovation to provide on stack and archotecture. The last part will focus on the co-integration of stretchable sensors and flexible/semi-rigid elements.

Analysis and modeling the evolution of new active materials during the first charge-discharge cycles of a Li-Ion battery

Département de l'Electricité et de l'Hydrogène pour les Transports (LITEN)

Laboratoire Modélisation multi-échelle et suivi Performance



Facing the energy transition, the storage of energy is a major issue. However, it appears necessary to increase the battery storage capacity and one way could be the use of silicon in addition to graphite for the negative electrode of Li-ion accumulators. The development of accumulators based on these materials is however slowed by their instability, related to the swelling of silicon during the insertion of lithium. Thus, the understanding of the phenomena occurring during the first cycles of operation appear fundamental to master the operation over the long term. This thesis project aims to understand and model the mechanical behavior of these new silicon-graphite electrodes. It is based on 3 teams: in Saclay, we will synthesize custom materials: silicon nanoparticles, silicon / germanium alloys, core @ shell where the shell will be carbon. Commercial silicon / graphite materials will also be used as reference. The behavior of materials will be studied in Grenoble using a laboratory diffractometer allowing in-situ and operando analyzes and large instruments such as ESRF or SOLEIL. These measurements will provide information on the stress inside the silicon but also on the state of lithiation of the graphite and will allow the modeling of the electrochemistry of the insertion of the lithium in the silicon, in particular the dependence in time of the hysteresis, still poorly understood. The aim of the thesis is to build a physics-based battery model allowing: "simple" experiments of swelling measurements, electrical cell performance measurements, early-life and model cycling and modelization, to deduce the mechanical and electrochemical behavior of cells at the scale of grains and agglomerates. This, in order to predict the aging of cells in the long term, in relation with their mechanical properties.

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