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

PhD : selection by topics

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Electrochemical deposition of insulating polymer films

Département des Plateformes Technologiques (LETI)

Laboratoire

01-10-2020

SL-DRT-20-0308

paul.haumesser@cea.fr

Emerging materials and processes for nanotechnologies and microelectronics (.pdf)

The electrohoretic deposition is a well known technique to form polymeric coatings with a variety of materials such as polyetherimide (PEI). This technique usually requires the application of several (tens of) volts. Under such conditions, electrochemical reactions occur at the electrodes, such as solvent decomposition, that promote polymer precipitation at their surface. Recent results suggest that these electrochemical reactions are sufficiently active at much lower overpotentials (below 3V). This would enable deposition processes under mild conditions with improved control over the film properties. In this thesis, the mechanisms at play during the deposition of PEI under such mild conditions will be studied, with the aim of developing a process suitable for the fabrication of capacitors with high breakdown voltage. This approach will also be extended to other insulating polymers compatible with healthcare applications (such as packaging of wiring circuits for implant systems) or to hydrophilic and/or porous polymers for the encapsulation of biologic structures (cells, enzymes, bacteria) or cell filtration in biochips.

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Characterization of all-solid-state batteries using neutron and synchrotron facilities

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

Laboratoire Matériaux

01-10-2019

SL-DRT-20-0317

lionel.picard@cea.fr

Advanced nano characterization (.pdf)

In view to increasing both energy density and safety of lithium batteries, all solid state battery systems are currently of interest, either based on the use of polymer or inorganic electrolyte materials, or the combination of them as hybrid electrolytes. Research activities in this field are already well established at CEA-Grenoble, such as the developments of ionic conductive ceramic materials and single-ion conductive polymers. In this frame, the PhD student will aim at supporting this work through better understanding of the hybrid electrolyte system. The objectives of the PhD student will be the in depth characterisation of the structure and properties of such systems, including local/nanoscale organisation, organic-inorganic interfaces and electrolyte-electrode interfaces. The studies will use materials already available at CEA and novel cathodes from UMICORE, as well as new material under development. The student will employ cutting-edge neutron and synchrotron techniques, such as small angle scattering, tomography, micro-beam and imaging techniques, to characterise the hybrid materials both ex situ and operando in devices and propose potential optimisation to the systems.

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Unsupervised deep learning methods for side-channel attacks

Département Systèmes (LETI)

Centre d'Evaluation de la Sécurité des Technologies de l'Information

01-09-2020

SL-DRT-20-0324

eleonora.cagli@cea.fr

Cyber security : hardware and sofware (.pdf)

Secure components exploiting embedded cryptographic mechanisms, for instance smart cards, may be vulnerable to the side-channel attacks. Such attacks are based onto the observation of some physical features measured during the device activity, such as power consumption, electromagnetic irradiation, execution time? the variation of these quantity may provoke an information leakage. A deep analysis of the leakage may lead an attacker to retrieve sensitive information, for instance the secret keys of the embedded cryptographic algorithms, and so to break the device security. In order to analyze the leakages, which are typically collected as high-dimensional signals big datasets, the deep-learning methods are nowadays a privileged tool. Since 2016, the interest of embedded security researchers toward this topic grows very fast, especially because of the efficiency of these methods in the context of profiled attacks. In this context, the attacker has access to a second dataset, over which he has complete knowledge. This second dataset allows him to perform a preliminary supervised training phase. This context is the most advantageous for the attacker. To setup the attacks on the field, for instance in the context of complex secure systems evaluation, this scenario is not available. In the wide state-of-the-art concerning non-supervised attacks, machine-learning techniques appeared about ten years ago. In particular clustering methods attracted considerable interest. Today, the deep-learning research makes clustering algorithms evolve, in particular through ?embedding? techniques. These techniques aim at represent data into a space that enhances certain ?useful? relations among data. The principal application domain of these techniques today is the representation of words for the natural language analysis: a useful representation should embed words into a space where words belonging to the same semantic field are close to each other. The goal of this research is studying ?deep embedding? techniques, evaluating their suitability for non-profiled attack scenarios, in particular in the context of public key cryptographic algorithms, formalizing an efficient deep-clustering-based attack strategy and deeply analyzing its properties.

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Development of high-performance NdFeB permanent magnet using Powder Injection Moulding

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire de Formulation des Matériaux

01-11-2020

SL-DRT-20-0329

sebastien.rolere@cea.fr

Energy efficiency for smart buildings, electrical mobility and industrial processes (.pdf)

Due to their remarkable magnetic properties, permanent magnets made of NdFeB alloys are an important part of the Energy Transition, with several applications in Energy (wind turbines) and in Transport (electric vehicle) sectors, for example. NdFeB magnets are usually produced by powder compression and sintering, and complex shapes are obtained through expensive machining operations. The powder injection moulding (PIM) process allows the direct production of parts with complex geometries through the conventional plastics processing technics, and is a way to reduce both machining operations and waste materials. Therefore, PIM is currently under consideration for the manufacturing of permanent NdFeB magnets with high density and magnetic performances and complex geometries. Nevertheless, the use of organic polymer binders for injection moulding (i), and the post-injection chemical and physical debinding steps (ii) during the PIM procedure, can be responsible of potential organic (i.e. carbon and/or oxygen) contaminations of the NdFeB powder, and consequently, of a significant degradation of magnetic properties of the magnets. Each of these contributions needs to be in-depth studied, for optimizing the magnetic properties of injection-moulded permanent NdFeB magnets. In particular, the understanding of the physicochemical interactions between polymer binders (and/or their degradation products) with the NdFeB powders, should lead to the development of feedstocks compatible with the injection moulding of low-contaminated permanent magnets.

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Concrete Programming Model for computer with quantum accelerator

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Infrastructure et Ateliers Logiciels pour Puces

01-10-2020

SL-DRT-20-0364

Henri-Pierre.Charles@cea.fr

New computing paradigms, circuits and technologies, incl. quantum (.pdf)

Quantum computers will provide unprecedent performances thanks to a very different computing model from the classic computers. The information medium is no longer a 2 states bit but a qbit carrying analog information. Besides, the possibility of entangle a multitude of qbits and manipulate them in a coherent way will provide unprecedented computing power. These quantum computers, with specific applications, will be accelerators of for conventional computers and can not carry a full application. This type of heterogeneous architecture already exists: a GPU or a DSP are pro- grammed from a conventional processor. But in this case the calculation models are similar and the data use the same representation: the two's complement binary format to integer numbers, the IEEE 754 format for floating point numbers, UNICODE for characters, etc. In a quantum machine (as in the vision of DELFT University [3]), it will be necessary to mix two types of very different calculation models (Von Neumann and Quantum models) and data representation spaces that are also different. This thesis will explore different calculation models and ways to move from one model to the other. A programming language and tools for compilation to implement algorithms and make them operate on different platforms (hardware or simulated) will be the main outcome of the thesis. The candidate will have to learn and synthesize a certain number of knowledge: current quantum machines (via platforms of simulation and/or real machines), take into account the characteristics of the physical qbits performed at LETI, discover the calculation models adapted to quantum computation (ZX calculus [2]), assimilate the algorithms / applications [6] known in the quantum field. The subject is pluridisciplinary complex, but CEA is an ecosystem where all this knowledge is present both in the design of physical qbits, in the design of physical qbits, in the electronic, in terms of computer architecture and languages and UGA will provide knowledge, both at the algorithmic level and at the model level of programming level. Through the synthesis of knowledge, the candidate will propose new way to program quantum accelerators in connection with current programming languages [7] based on pre-existing models such as the calculated ZX [2]. With a classic part for the control and access to data and a quantum part for the accelerated part of the program. The classical applications [6] of the domain can be used as benchmarks and will demonstrate the value of the approach, other algorithms will be studied to identify possible candidate for quantum acceleration. [1] H. Bohuslavskyi, A. G. M. Jansen, S. Barraud, V. Barral, M. Cassé, L. Le Guevel, X. Jehl, L. Hutin, B. Bertrand, G. Billiot, G. Pillonnet, F. Arnaud, P. Galy, S. De Franceschi, M. Vinet, and M. Sanquer. Cryogenic subthreshold swing saturation in fd-soi mosfets described with band broadening. IEEE Electron Device Letters, 40(5):784787, May 2019. 3 [2] Niel de Beaudrap and Dominic Horsman. The ZX calculus is a language for surface code lattice surgery. arXiv preprint arXiv:1704.08670, 2017. [3] X. Fu, L. Riesebos, L. Lao, C. G. Almudever, F. Sebastiano, R. Versluis, E. Charbon, and K. Bertels. A Heterogeneous Quantum Computer Architecture. In Proceedings of the ACM International Conference on Computing Frontiers, CF '16, pages 323 330, New York, NY, USA, 2016. ACM. [4] Harald Homulle, Stefan Visser, Bishnu Patra, Giorgio Ferrari, Enrico Prati, Car- men G. Almudéver, Koen Bertels, Fabio Sebastiano, and Edoardo Charbon. Cry- oCMOS Hardware Technology a Classical Infrastructure for a Scalable Quantum Computer. In Proceedings of the ACM International Conference on Computing Frontiers, CF '16, pages 282287, New York, NY, USA, 2016. ACM. [5] Louis Hutin, Benoit Bertrand, Yann-Michel Niquet, Jean-Michel Hartmann, Marc Sanquer, Silvano De Franceschi, Tristan Meunier, and Maud Vinet. SOI MOS Technology for Spin Qubits. ECS Transactions, 93(1):3536, October 2019. [6] Ashley Montanaro. Quantum algorithms: an overview. November 2015. [7] Benoît Valiron, Neil J. Ross, Peter Selinger, D. Scott Alexander, and Jonathan M. Smith. Programming the quantum future. Communications of the ACM, 58(8):52 61, 2015. [8] Rodney Van Meter and Clare Horsman. A Blueprint for Building a Quantum Com- puter. Commun. ACM, 56(10):8493, October 2013.

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Investigation of manufacturing process related structure and performance of fuel cell electrode

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

Laboratoire Composants Pemfc

01-10-2020

SL-DRT-20-0365

arnaud.morin@cea.fr

Advanced hydrogen and fuel-cells solutions for energy transition (.pdf)

Zero emission automotive using hydrogen as a fuel and powered by a proton exchange membrane (PEM) fuel cell are now commercially available. However, large-scale commercialization of PEM fuel cell vehicles requires progress in performance, cost and durability, for which the electrode is the most limiting component. It is made of a random assembly of platinum based nanoparticles within a proton conducting polymer network. The electrode is obtained from a slurry after evaporating the solvents. Currently, research and development to improve the performance of the electrode and reduce the cost of manufacturing rely on a trial and error basis. The goal of this project is to increase the knowledge on the relationships between ink composition, electrode structure, properties and performance. The evolution of the ink during the drying process and the so obtained electrode will be characterized using neutron and X-Ray scattering, as complementary tools to unravel the organization of the catalyst material and of the polymer. By correlating these results with Operando electrochemical, structural and imaging measurements, we aim at rationalizing the design of the electrodes. This project involves partners having all the complementary skills needed for this study of most interest for the industrial partner, which is a leader in the research, development and production of fuel cell cars.

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