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

PhD : selection by topics

A quantum algorithm to compute classical Worst-Case Execution Time (WCET)

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Calcul Embarqué

01-10-2018

SL-DRT-18-0365

sergiu.carpov@cea.fr

Worst Case Execution Time (WCET) are important data elements to feed any safety and schedulability analysis of safety critical real-time systems for which any default can jeopardize the life of the system or even threaten the life of human beings. WCET is particularly important in the context of real-time autonomous systems (e.g. robotics, self-driving cars). The problem of computing safe upper bonds of execution time is well known, but the challenge is also to have them tight to avoid over-engineering of real-time systems and mastering their costs. But this challenge is still not fully reached and moreover tends to pursue a moving target as the hardware and software architecture of real-time systems also moves forward (pipelines, cache memories, multi-cores, etc.). The goal of this PhD Thesis is to explore what quantum computing can do to simplify the problem, bring for more precision and capacity of analysis of these issues. This work will be supported by existing state of the art, and could explore a bit further than the strict domain of usual WCET analysis. Depending on the candidate profile, the subject will bend more towards either implementation aspects - How to best implement a WCET algorithm onto an available quantum simulator (eg. QX simulator, Quantum Learning Machine), or computational complexity theory aspects.

Development of innovative piezoelectric micromachined ultrasound transducer (pMUT) for automotive applications

Département Composants Silicium (LETI)

Laboratoire Composants Micro-Capteurs

01-09-2018

SL-DRT-18-0471

bruno.fain@cea.fr

The potential use of piezoelectric micromachined ultrasound transducers (pMUT) within smartphones, tablets and connected devices have raised a growing interest during the last years to build new fingerprint sensors and achieve better, low-power range-finder. To meet the specific needs of these new applications, the performances of pMUT have to be increased. This Ph.D. thesis aims at building new devices to cope with the requirements of automobile applications. The conception, the fabrication and the characterization of the pMUT will be investigated by the Ph.D student. The conception will be based on both analytical approaches and finite elements modelling (ANSYS, Comsol Multiphysics). The fabrication process will be achieved within the 8 inches MEMS Platform of CEA-LETI with the strong support of the CEA teams. The characterization, mostly probe measurements at the wafer level, will confirm and refine the models. The relevance of the devices for the targeted applications will be evaluated. For this purpose, the Ph.D. student is expected to have strong background in mechanics. He will tackle both scientific and technological challenges. He should be an autonomous team player.

Analog to Digital Converter for Neural Network based Acoustic Detection System for IoT Systems

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Architectures Intégrées Radiofréquences

01-09-2018

SL-DRT-18-0494

dominique.morche@cea.fr

The purpose of this PhD is to develop a new analog to digital converter whose output will be some trains of spike. This waveform is particularly adapted to the neural network processing. A joint optimization between the ADC and the neural network will be done by the PhD. Signal Detection and Classification is becoming a key function in the internet of things to extract useful information from the environment. For such functionality, neural network based processing is becoming more and more interesting. However, the power efficiency is often limited by the analog to digital interface which is mandatory. Therefore, more power efficient analog to digital interface are required and their power consumption should adapt itself to the application requirements. That is the reason why, the purpose of this PhD is to develop a new analog to digital converter whose output will be some trains of spike. This waveform is particularly adapted to the neural network processing. A joint optimization between the ADC and the neural network will be done by the PhD, in collaboration with a Post-Doc who will be working on the digital part. The design will exploit the 28nm FD-SOI technology developed by STMicroelectronics. Several circuit will be design, fabricated and tested. The objective at the end is to build a demonstrator able to distinguish audio signals. Industrials use cases will be considered. The Phd will be done in collaboration with IMT Atlantique. The PhD will be asked to present his work in the scope of collaborative European project.

Adaptive CMOS Image Sensor for vision systems

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Circuits Intégrés, Intelligents pour l'Image

01-01-2018

SL-DRT-18-0507

gilles.sicard@cea.fr

The aim of this thesis is to explore new kind of smart vision sensor architectures using for enhance the sensor reactivity and for simplify the image processing. The studied vision system will use new 3D microelectronic technologies from CEA-leti to perform both an image acquisition and a real time local adaptation to optimize the pixel setup to its using environment. The PhD student will benefit during his 3-years thesis of the expertise and the scientific excellence of the CEA leti to attend objectives with a high level of innovation through international patents and publications. These technologies are capable to stack several integrated circuits. The main advantage is to propose a high density of interconnections between them, allowing connection at the pixel level. The aim of the adaptive system is to control the pixel (or group of pixels) setup to optimize its functioning and regularize the output image. The dynamic and autonomous candidate, will have a microelectronic master degree, specialized in analog integrated circuit design. A good knowledge of circuit design CAD tools will be important (Cadence, and also Matlab) and good knowledge in image processing will be appreciated. This thesis will start with the state of the art study, then the PhD student will define the optimal architecture. Finally, a test chip will be designed and tested. It will demonstrate the scientific and industrial potentialities of the proposed solutions.

Département d'Optronique (LETI)

Laboratoire d'integration technologique pour la photonique

01-10-2018

SL-DRT-18-0517

francois.boulard@cea.fr

Adaptive Compress sensing radiofrequency solution for feature extraction and direct classification of RADAR signal

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Architectures Intégrées Radiofréquences

01-01-2018

SL-DRT-18-0520

michael.pelissier@cea.fr

This PhD subject aims to develop and line up a radiofrequency solution relying on compress sensing acquisition tailored to feature extraction and direct classification of RADAR signal. Most relevant signals can be compressed: our smartphones are filled of photos taken with multi-million pixels-camera, which, after compression occupy only a few thousand bytes. We can wonder what are the benefits to acquire a huge volume of information if only a small part of it is really relevant? The sparse sampling and the compressive sensing (CS) acquisition suggest to answer the question by extracting the relevant information directly at the acquisition front end level. The problem is fully transferable to radiofrequency receiver at the core of our mobile equipment used daily (Wifi, bluetooth, 3G, 4G ?). Thus the paradigm is switching from "analog to digital converter" to "analog to information converter" or "analog to feature converter" to the extent that we seek to convert the relevant information or signal features rather than the raw signal itself. CS methods find possible application in RADAR system whose demand is pushing up by the development of autonomous system. Within this context, the objective consist in extracting the environment signature for radio-identification application for instance or health monitoring. This PhD subject aims to develop and line up a radiofrequency solution relying on compress sensing acquisition tailored to feature extraction and direct classification of RADAR signal.

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