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

PhD : selection by topics

ultra low temperature solid phase recrystallization assited by nanosecond laser annealing

Département des Plateformes Technologiques (LETI)




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

During last years, great progress has been made in reducing the thermal budget required for the manufacture of microelectronics devices. Moreover, nanosecond laser annealing represents a very promising alternative for the integration of microelectronic devices whose thermal budget must be limited. Since very few years, CEA/LETI has started a very ambitious program on advanced thermal treatments for microelectronics. In this context, a nanosecond laser annealing equipment has been installed in the LETI clean room. This innovative process makes it possible to reach very high temperatures for extremely short durations (a few tens of ns). This implies that the thermal budget applied to the irradiated structures is very low. It has recently been demonstrated that nanosecond laser annealing can be used to obtain solid phase recrystallization of partially amorphized silicon layers. This method can be used to optimize different steps of the manufacturing processes, as for exemple dopant activation on source and drain. It is therefore fundamental to understand the physical mechanisms and to explore the impact of different parameters on the recrystallization kinetics in order to manage this process in basic materials such as Si and SiGe. This thesis aims evaluating the contribution of nanosecond laser annealing on the structural and electrical properties of different semiconductor stacks

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Lensless imaging and artificial intelligence for rapid diagnosis of infections

Département Microtechnologies pour la Biologie et la Santé (LETI)

Laboratoire Systèmes d'Imagerie pour le Vivant



Artificial intelligence & Data intelligence (.pdf)

The objective of the thesis is to develop a portable technology for pathogen identification. Indeed, in a context of spread of medical deserts and resurgence of antibiotic-resistant infections, it is urgent to develop innovative techniques for rapid diagnosis of infections in isolated regions. Among optical techniques for pathogen identification, lens free imaging methods draws attention because they are the only ones currently able to offer simultaneous characterization of a large number of colonies, all with low-cost, portable and energy-efficient technology. The objective of the thesis is to explore the potential of lensless imaging combined with artificial intelligence algorithms to identify bacterial colonies present in a biological fluid. The thesis will aim to optimize the sizing of the imaging system (sources, sensors) and to study image processing and machine learning algorithms necessary for colony identification. Two cases of clinical applications will be studied.

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Improvement of CdZnTe based gamma imager CdZnTe using machine learning

Département d'Optronique (LETI)

Laboratoire Architecture Systèmes Photoniques



Photonics, Imaging and displays (.pdf)

Gamma imaging is a technique widely used in medical imaging (molecular imaging, nuclear medicine) and security (transportation, industry). CdZnTe semiconducting detectors usage is currently emerging for SPECT (Single Photon Emission Computed Tomography, using gamma-cameras) and portable gamma imaging. Indeed, they enable performance improvements in speed, sensitivity and image quality. These detectors operate at room temperature and are sensitive to five physical parameters of the interaction: deposited energy E, interaction time T and the 3-dimensional position XYZ. These parameters are estimated by real-time analysis of anode electronics signals. However, the link between electrical signals and physical parameters is not fully known, as material physical properties are not uniform inside detector. The goal of this Ph.D. internship is to overcome these limits by using machine learning techniques to model actual detector response. Recent multi-layered deep learning technique enable to build and train complex and flexible system models, and to overcome our lack of knowledge about detector physics. The identification of internal physical parameters of the detector would allow to optimize estimation of interaction location, time and energy. This will lead to a better image quality and then capability to detect small and weak objects, enabling better diagnoses and lower false alarm rate. The student may have a background in applied mathematics (machine learning) and/or instrumentation physics. He/she need to have taste for multi-disciplinary research, mixing experimental physics and data science.

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Ecodesign methodology for new generations of batteries

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire des Eco-procédés et EnVironnement



Electrochemical energy storage incl. batteries for energy transition (.pdf)

The development of the electrification of vehicles requires the design of cheaper and more efficient battery technologies. In response to this demand, many development paths are under study, such as new generations of Li-ion with reduced cobalt content or high energy density, all solid state lithium batteries or Li-Sulphur batteries, among other. Apart from the performance aspect, there is a real need to assess the environmental impact of these technologies over their entire life cycle (LCA), and to look at eco-design options for the development of the batteries of the future. The proposed thesis will aim at addressing these issues, using a multidisciplinary approach combining the skills of at least three laboratories from CEA LITEN. At the end of the thesis, the expected results will be: an environmental evaluation of the 3 new generation of battery technologies (advanced Li-Ion, Li-S and All-Solid), compared to reference battery technologies as well as an eco-design methodology to guide decision support in the development of low TRL battery technologies.

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Applied formal semantics in hardware compiler frameworks

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire composants logiciels pour la Sûreté et la Sécurité des Systèmes



Artificial intelligence & Data intelligence (.pdf)

The development of RISC-V instruction set architecture (ISA) is supported by new methodologies and tools which are dedicated to increase the productivity of hardware designs (i.e., high-level design languages and specialized compilation chains). At the language level, Chisel and FIRRTL Hardware Description Languages (HDLs) aim to raise the level of abstraction of hardware design. It thus becomes appealing to formally reason on functional and temporal properties of these high-level designs and rely on appropriate compilation extensions to transfer these high-level properties down to the level of generated Verilog, for example. In this PhD proposal, we target a formal verification framework for computer architectures to support the specification and verification of timing-related safety and security properties. The following two contributions are expected of this PhD thesis: 1) the design and implementation of a verification infrastructure based on formal executable semantics of Chisel and FIRRTL HDLs and 2) the design and implementation of an assertion language to express timing safety and security properties, which are to be verified with the aforementioned formal infrastructure. The scientific contributions of this thesis are expected to evaluated on a selection of the rich-set of architecture designs provided by the RISC-V ecosystem.

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Porous structures of hydrogenated nanodiamonds for CO2 transformation in exploitable products

Département Métrologie Instrumentation et Information (LIST)

Laboratoire Capteurs Diamants



Green and decarbonated energy incl. bioprocesses and waste valorization (.pdf)

Since its identification as a source of solvated electrons usable for photocatalysis, hydrogenated bulk diamond is actively investigated for CO2 reduction. This behaviour is conferred by the C-H dipoles present at the surface that favor the electron emission to the interface with surrounding media. Moreover, its electronic structure (negative electron affinity) allows the emission of photoelectrons, able to initiate the CO2 reduction at one electron or to form solvated electrons. Hydrogenated nanodiamonds behave a similar electronic structure as we demonstrated few years ago. This PhD aims first to elaborate porous materials using diamond nanoparticles via innovative and scalable technics to allow a tunable and efficient use for CO2 reduction. Performances for CO2 reduction will be then evaluated while mechanisms involved in the production of reducing species under illumination will be investigated via a more fundamental approach. The first barrier concerns the elaboration of nanocomposite porous matrix fabricated from hydrogenated diamond particles for photo(electro)catalysis. An original process (HIMAYALAN) developed at LEDNA, combining nanoparticles jets under vacuum to magnetron sputtering, will be used. Porous layers of nanoparticles embedded in another material (silica or amorphous carbon) will be fabricated exhibiting a very high porosity. A co-doping of such composites with metallic particles is also performed to improve the optical absorption performances. A proof of concept is currently under progress with the Bottom-up project CORAIL. The second obstacle corresponds to the boron doping of diamond particles (size 10 to 200 nm) which confers it an electrochemical activity. In that case, their catalytic efficiency can be enhanced applying a bias. Different synthesis routes are considered: from the milling of boron doped diamond films (commercial particles) to a more innovative and scalable approach based on the synthesis of core shell boron doped diamond. The former process patented at LCD will be developed during an ANR PRCE project starting in April 2020. The second aspect of this PhD concerns the evaluation of porous nanocomposite diamond layers for the CO2 reduction via photo(electro)catalysis. A dedicated set-up will be developed at LCD including a lamp and the ability to work under CO2 pressure. The crystalline structure and the properties of hydrogenated boron doped diamond particles will be investigated using SOLEIL Synchrotron facilities. Relations with their photocatalytic performances will allow to improve their efficiency. XPS studies on isolated particles will be achieved on PLEIADES beamline to extract the surface structure at the atomic level and the location of heteroatoms. Photo-ionisation and photo-fragmentation studies versus the wavelength of incident radiation will be performed on DESIRS beamline.

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