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

PhD : selection by topics

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Piezoelectric MEMS actuator hydraulically amplified

Département Composants Silicium (LETI)

Labo Composants Micro-actuateurs

01-09-2020

SL-DRT-20-0488

laurent.mollard@cea.fr

Cyber physical systems - sensors and actuators (.pdf)

The main objective of micro-actuators research is an architecture that can generate large displacements and forces over a wide frequency range, while not consuming a significant amount of electrical power. To date, no solution meets all these criteria. Indeed hydraulic actuators do not meet the criterion of compactness and frequency but allow significant force and displacement. Similarly, electromagnetic actuators have a good frequency range with excellent force and stroke output, but they are generally heavy and require significant electrical current. Piezoelectrics are also known for their excellent operating bandwidth and can generate large forces in a compact size, but traditionally they have very small displacements. The technological breakthrough of the thesis will consist to develop a hydraulic amplification mechanism, by applying small displacements on a large surface, sa as to move a liquid, and to generate, by conservation of the volume, important displacements on a weaker moving surface. Therefore, the thesis will consist to develop and integrate into a MEMS (Micro Electro-Mechanical System) system, this hydraulically amplified piezoelectric actuator (called HDAM system for "Hydraulic Displacement Amplification Mechanism") and optimize it

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ultra low temperature solid phase recrystallization assited by nanosecond laser annealing

Département des Plateformes Technologiques (LETI)

Laboratoire

01-09-2020

SL-DRT-20-0514

Pablo.ACOSTAALBA@cea.fr

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

01-10-2020

SL-DRT-20-0518

caroline.paulus@cea.fr

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

01-11-2020

SL-DRT-20-0522

gmontemont@cea.fr

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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Adaptative frequency tuning electronic systems for broadband vibration energy harvesting

Département Systèmes (LETI)

Laboratoire Autonomie et Intégration des Capteurs

01-09-2020

SL-DRT-20-0530

pierre.gasnier@cea.fr

Cyber physical systems - sensors and actuators (.pdf)

Energy harvesting is a theme whose aim is to supply power to wireless sensor nodes by replacing the source of electrical energy (battery, cables) with ambient energy. Vibration energy harvesting, in particular, makes it possible to exploit the mechanical energy of an environment and convert it into electricity in order to supply a wireless sensor node. The thesis will focus on the exploitation of piezoelectric materials on resonant structures to convert vibration energy into electricity. The use of mechanical resonators amplifies ambient vibrations, but the harvested power drops sharply when the spectrum of ambient vibrations no longer coincides with the harvester's resonant frequency. For the adoption of this type of system by industry, one of the major obstacles is therefore this frequency selectivity. The CEA and the University of Savoie Mont-Blanc (SYMME Laboratory) have recently proposed high-performance techniques to solve this problem by using harvesters that can be dynamically tuned by an electronic system. Indeed, coupled with intelligent electronics, a "strongly coupled" harvester has its mechanical behavior modified (its resonance frequency in particular), making it possible to follow the evolution of the input frequency. The objective of the thesis is to propose, dimension, simulate, fabricate and test innovative electronic architectures (based on discrete components and/or microcontrollers) allowing the automatic tuning and the search for the maximum power point of piezoelectric vibration energy harvesters. Particular attention will be paid to the low power and small size of the electronic architectures since the ultimate goal is to propose an autonomous circuit consuming a negligible part of the harvested electrical energy. At the end of the thesis, the selected architecture(s) will then be proposed to the CEA-Leti's integrated circuit department for miniaturization. A complete demonstrator (harvester, micro-converter and adjustment circuit) is targeted for the end of the thesis.

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Ecodesign methodology for new generations of batteries

Département des Technologies des NanoMatériaux (LITEN)

Laboratoire des Eco-procédés et EnVironnement

01-10-2020

SL-DRT-20-0535

elise.monnier@cea.fr

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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