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

PhD : selection by topics

Additive manufacturing of a high temperature strain jauge

DLORR (CTReg)

Autre DLORR

01-10-2020

SL-DRT-20-0217

manuel.fendler@cea.fr

The Internet of Things brings intelligence and connectivity within industrial tools. It gathers a real-time knowledge of the equipment parameters, which allows optimizing the processes by a better control and monitoring of the manufacturing conditions. The accumulation of data allows statistical processing by machine learning to improve the process and control in real time thanks to more connectivity and embedded intelligence. At the heart of data collection in the tools, many sensors are designed based on a common sensing element: the strain gauge. However, the operating conditions in the industrial environment are extremely severe; the major degradation stimulus is temperature, with values commonly exceeding 400 ° C, eliminating the use of gauges that are performed exclusively on plastic substrates. The aim of this thesis is to develop high temperature gauge sensors, leveraging additive techniques for both the fabrication and integration of gauges on topologically optimized test bodies.

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Contactless electronics under high-temperature and radiation exposures

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Intégration Gestion d'Energie Capteurs et Actionneurs

SL-DRT-20-0249

gael.pillonnet@cea.fr

The objective is to design a new generation of contactless electronics to be robust to high-temperature and radiation expositions. Based on the recently introduced ?contactless electronics? paradigm, the PhD student have to define new mechanical structures and electronics schemes to operate in harsh conditions and to offer analog- and digital-operations. This study is based on a complete breakthrough proposal compared to the classical transistor-based electronics to overcome the inherent physical limit of transistor at high-temperature. The PhD student will propose, model and simulate electro-mechanical micro fabricated structures to validate the theoretical principle recently announced by some senior-scientists in our laboratory. The project involves multi-disciplinary study including microelectronics, electromechanical MEMS devices, solid-state physics and gives an excellent opportunity for PhD student to cover a large scientific scope.

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AI processing of time series for smart sensors

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Infrastructure et Ateliers Logiciels pour Puces

01-10-2020

SL-DRT-20-0261

marielle.malfante@cea.fr

Today, sensors are used to acquire data of a given modality (acoustics, pressure, image, etc.). Usually, such data are stored before being analysed, for instance with machine learning methods. The relevant information is thereby extracted. A large variety of sensors and use cases can be considered: ? Microphones for the automatic classification of acoustic landscapes, ? Pressure sensors to study deformation and monitor various architectures (brides, dams, wind turbines, etc.), ? Seismometers to detect signals warning of a seisms or of a volcanic eruption, ? Smart watches or bracelets to detect stress phases, ? Etc. The issue of smart sensors consists in creating and designing sensors whose output is the relevant information, straightaway (see Fig. 1). Most of the time the raw signal no longer has to be transferred and stored. Smart sensors are a challenge in numerous fields, typically when sensors have to run autonomously in remote environments, or with limited power and storage access. For instance when studying acoustic landscapes pour environmental monitoring (forest, underwater areas, etc). IoT and wearable sensors are also targeted. Turning a sensor into a smart sensor presents a challenge at many levels. For instance, efficient AI methods to process the data need to be designed, with constraints in term of computation power and energy. Another challenge consists in building those analysis tools from small datasets, or from weakly supervised datasets. CEA is already conducting researches on those issues, and AI based methods are particularly relevant. This PhD subjects focuses on sensors recording time series: IMU, microphones, connected bracelets, etc. The core of the issue is to work on AI methods for time series, in one or several applicative fields. The PhD registers in a larger subject, namely AI reliability (anomaly detection, detection of events of class unseen during the training stage, etc.), but also the development of AI methods under labelling constraint. The topic is ambitious and several approaches are considered.

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Realization by additive manufacturing of a 3D ceramic / metal device, applied to remote power transfer and to remote control.

DMIPY (CTReg)

Autre DMIPY

01-09-2020

SL-DRT-20-0282

regis.delsol@cea.fr

CEA Tech's materials platform focuses on the shaping of advanced ceramics and offers R&D partnerships in additive manufacturing involving ceramics parts. The proposed thesis aims the increase of the knowledge and expertise needed to design and realize ceramic / metal devices. The chosen application is the remote control and the remote power transfer of a mechatronic system consisting of one or several sensors. The first phase of the thesis of a duration of 9 months will consist in a bibliography study and a dimensioning study in order to choose the best ceramic / metal couple with respect to the application case. The second phase of a duration of 9 months will consist in additive manufacturing and metallization of planar prototypes and will consist in mechanical, morphological and dielectric characterization. The third phase of a duration of 12 months will lead to additive manufacturing and metallization of the final 3D prototype. Eventually, the performance of the prototype will be evaluated though functional testing.

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Integration of piezoelectric-based power converters

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Intégration Gestion d'Energie Capteurs et Actionneurs

SL-DRT-20-0286

adrien.morel@cea.fr

The aim of this thesis is to integrate high-efficiency power converters based on resonating piezoelectric transducers. A large part of the work is to develop the integrated circuit to handle high switching frequency operation while maintaining an adiabatic energy transfer. Based on our recently published results [Pollet2019], the integration of the power stage and the control between phases paves the way of the miniaturization of the piezoelectric transducer using microelectronics process. The PhD student will cover the sizing, IC design, electro-mechanical characterization and feedback control of miniaturized piezoelectric-based power converters. [Pollet2019] B. Pollet et al., A New Non-Isolated Low-Power Inductorless Piezoelectric DC?DC Converter, Trans. on Power Electronics, 2019.

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Embedded AI for the semantic interpretation of a probabilistic environment model

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Infrastructure et Ateliers Logiciels pour Puces

01-10-2020

SL-DRT-20-0291

tiana.rakotovao@cea.fr

The perception and modelling of an environment is a major issue when developing autonomous vehicles. How to model the surroundings of a vehicle? How to detect and identify the various obstacles? What about free spaces, and areas safe to drive on? Which sensor combination is the most appropriate to reach an exhaustive description and modelling of the environment? Those questions all have beginnings of answers, but still remain open and not yet solved. There also is a strong constraint regarding the need for embedding systems, which is one of the CEA focuses. Which processing and analysis can be considered while targeting embedded systems? Occupancy Grid is a model used to represent the surroundings of a vehicle and present various advantages. Several sensors of various modalities are used to compute the grid: each modality brings a specific information. For instance, infra-red is efficient by night, LIDAR offers a 360° field of view but is not robust to bad weather conditions, in which case a radar would be preferable. Ultrasound sensors on the contrary are used to analyse very short distances. CEA has developed approaches based on Bayesian fusion to produce SigmaFusion library. SigmaFusion is a tool to fuse the information of different sensors to produce an occupancy grid, which evolves with time. A strong point of SigmaFusion is the computing optimization: the technology is particularly efficient and competitive under strong embedded constraints (low cost integration with low energy consumption on micro-controller certified for critical task for the automotive market). An issue currently addressed is the use of EdgeAI methods to gain a semantic interpretation of an occupancy grid. A typical question is the level of knowledge and interpretation that can be reached while respecting the embedding constraint. Is it possible to detect the object evolving in a grid automatically, in real time and at low energetic cost (pedestrians, cyclists, cars, etc.)?

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