Developement of relaxed pseudo-substrate based on InGaN porosified by electrochemical anodisation
As part of the Carnot PIRLE project starting in early 2021, we are looking for a candidate for a post-doctoral position of 24 months (12 months renewable) with a specialty in material science. The project consists in developing a relaxed pseudo-substrate based on III-N materials for µLEDs applications, especially for emission in red wavelength. The work will focus on developing an InGaN-based epitaxy MOCVD growth process, on an innovative substrate based on electrochemically anodized and relaxed materials. He (She) will have characterize both the level of relaxation of the re-epitaxied layer and its crystalline quality. These two points will promote the epitaxial regrowth of an effective red LED. The candidate will be part of the team, working on the PIRLE project, will be associated to the work on red LED growth and its optical and electro-optical characterizations.
Post-doctoral position in AI safety and assurance at CEA LIST
The position is related to safety assessment and assurance of AI (Artificial Intelligence)-based systems that used machine-learning components during operation time for performing autonomy functions. Currently, for non-AI system, the safety is assessed prior to the system deployment and the safety assessment results are compiled into a safety case that remains valid through system life. For novel systems integrating AI components, particularly the self-learners systems, such engineering and assurance approach are not applicable as the system can exhibit new behavior in front of unknown situations during operation.
The goal of the postdoc will be to define an engineering approach to perform accurate safety assessment of AI systems. A second objective is to define assurance case artefacts (claims, evidences, etc.) to obtain & preserve justified confidence in the safety of the system through its lifetime, particularly for AI system with operational learning. The approach will be implemented in an open-source framework that it will be evaluated on industry-relevant applications.
The position holder will join a research and development team in a highly stimulating environment with unique opportunities to develop a strong technical and research portfolio. He will be required to collaborate with LSEA academic & industry partners, to contribute and manage national & EU projects, to prepare and submit scientific material for publication, to provide guidance to PhD students.
High precision robotic manipulation with reinforcement learning and Sim2Real
High precision robotic assembly that handles high product variability is a key part of an agile and a flexible manufacturing automation system. To date however, most of the existing systems are difficult to scale with product variability since they need precise models of the environment dynamics in order to be efficient. This information is not always easy to get.
Reinforcement learning based methods can be of interest in this situation. They do not rely on the environment dynamics and only need sample data from the system to learn a new manipulation skill. The main caveat is the efficiency of the data generation process.
In this post-doc, we propose to investigate the use of reinforcement learning based algorithms to solve high precision robotic assembly tasks. To handle the problem of sample generation we leverage the use of simulators and adopt a sim2real approach. The goal is to build a system than can solve tasks such as those proposed in the World Robot Challenge and tasks that the CEA’s industrial partners will provide.
Formalization of the area of responsibility of the actors of the electricity market
The CEA is currently developing a simulation tool which models the energy exchanges between the actors of the electricity market but which models, in addition, the exchanges of information between those actors. The first results of this work show that, for some new energy exchange schemes, ’indirect’ interactions between actors may appear and may cause financial damage (for example, the failure of a source of production of one actor may impact the income of another). Thus, the borders which clearly delimited until now the areas of responsibility of each actors could be brought to blur and their areas of responsibility could "overla". The candidate will be responsible for:
- Formally define the area of responsibility of an actor in the electricity market,
- Model the interactions, including ’indirect’ ones, that may appear between these actors,
- Apply formal proof techniques (such as ’model-checking’) to detect overlaps in areas of responsibility,
- Define the conditions of exchange between the actors which would guarantee the non-recovery of the areas of responsibility.
Non-volatile asynchronous magnetic SRAM design
In the applicative context of sensor nodes as in Internet of things (IoT) and for Cyber Physical Systems (CPS), normally-off systems are mainly in a sleeping state while waiting events such as timer alarms, sensor threshold crossing, RF or also energetic environment variations to wake up. To reduce power consumption or due to missing energy, the system may power off most of its components while sleeping. To maintain coherent information in memory, we aim at developing an embedded non-volatile memory component. Magnetic technologies are promising candidates to reach both low power consumption and high speed. Moreover, due to transient behavior, switching from sleeping to running state back and forth, asynchronous logic is a natural candidate for digital logic implementation. The position is thus targeting the design of an asynchronous magnetic SRAM in a 28nm technology process. The memory component will be developed down to layout view in order to precisely characterize power and timing performances and allow integration with an asynchronous processor. Designing such a component beyond current state of the art will allow substantial breakthrough in the field of autonomous systems.
Modeling of trapping and vertical leakage effects in GaN epitaxial substrates on Si
State of the art: Understanding and modeling vertical leakage currents and trapping effects in GaN substrates on Si are among the crucial subjects of studies aimed at improving the properties of GaN power components : current collapse and Vth instabilities reductions, reduction of the leakage current in the OFF state.
Many universities [Longobardi et al. ISPSD 2017 / Uren et al. IEEE TED 2018 / Lu et al. IEEE TED 2018] and industrials [Moens et al. ISPSD 2017] are trying to model vertical leakages but until now, no clear mechanism has emerged from this work to model them correctly over the entire range of voltage and temperatures targeted. In addition, modeling the effects of traps in the epitaxy is necessary for the establishment of a a robust and predictive TCAD model of device.
For LETI, the strategic interest of such a work is twofold: 1) Understanding and reducing the effects of traps in the epitaxy impacting the functioning of GaN devices on Si (current collapse, Vth instabilities…) 2) Reaching the leakage specifications @ 650V necessary for industrial applications.
The candidate will have to take charge in parallel of the electrical characterizations and the development of TCAD models:
A) Advanced electrical characterizations (I (V), I (t), substrate ramping, C (V)) as a function of temperature and illumination on epitaxial substrates or directly on finite components (HEMT, Diodes, TLM )
B) Establishment of a robust TCAD model integrating the different layers of the epitaxy in order to understand the effects of device instabilities (dynamic Vth, dynamic Ron, BTI)
C) Modeling of vertical conduction in epitaxy with the aim of reducing leakage currents at 650V
Finally, the candidate must be proactive in improving the different parts of the substrate
Measurement of active cell nematics by lensless microscopy
At CEA-Leti we have validated a video-lens-free microscopy platform by performing thousands of hours of real-time imaging observing varied cell types and culture conditions (e.g.: primary cells, human stem cells, fibroblasts, endothelial cells, epithelial cells, 2D/3D cell culture, etc.). And we have developed different algorithms to study major cell functions, i.e. cell adhesion and spreading, cell division, cell division orientation, and cell death.
The research project of the post-doc is to extend the analysis of the datasets produced by lens-free video microscopy. The post-doc will assist our partner in conducting the experimentations and will develop the necessary algorithms to reconstruct the images of the cell culture in different conditions. In particular, we will challenge the holographic reconstruction algorithms with the possibility to quantify the optical path difference (i.e. the refractive index multiplied by the thickness). Existing algorithms allow to quantify isolated cells. They will be further developed and assessed to quantify the formation of cell stacking in all three dimensions. These algorithms will have no Z-sectioning ability as e.g. confocal microscopy, only the optical path thickness will be measured.
We are looking people who have completed a PhD in image processing and/or deep learning with skills in the field of microscopy applied to biology.
Digital circuit design for In-Memory Computing in advanced Resistive-RAM NVM technology
For integrated circuits to be able to leverage the future “data deluge” coming from the cloud and cyber-physical systems, the historical scaling of Complementary-Metal-Oxide-Semiconductor (CMOS) devices is no longer the corner stone. At system-level, computing performance is now strongly power-limited and the main part of this power budget is consumed by data transfers between logic and memory circuit blocks in widespread Von-Neumann design architectures. An emerging computing paradigm solution overcoming this “memory wall” consists in processing the information in-situ, owing to In-Memory-Computing (IMC).
CEA-Leti launched a project on this topic, leveraging three key enabling technologies, under development at CEA-Leti: non-volatile resistive memory (RRAM), new energy-efficient nanowire transistors and 3D-monolithic integration [ArXiv 2012.00061]. A 3D In-Memory-Computing accelerator circuit will be designed, manufactured and measured, targeting a 20x reduction in (Energy x Delay) Product vs. Von-Neumann systems.
Advanced tandem time of flight mass spectrometry for microelectronic applications
The CEA LETI seeks to recruit a post-doctoral researcher to work on the development of advanced time of flight secondary ion mass spectrometry applications (TOF-SIMS). The candidate will work on a new TOF-SIMS instrument equipped with tandem MS spectrometry, in-situ FIB and Argon cluster sputtering. The research project will be focused around the following topics
• Developing methods to correlate TOF-SIMS with AFM, XPS and Auger
• Improving the sensitivity and efficiency of fragmention of the tandem MS spectrometer
• Developing 3D FIB-TOF-SIMS applications and improving the spatial resolution.
The candidate will also have access to the wide range of state of the art instruments present on the nanocharacterisation platform as well as bespoke samples coming from the advanced technology branches developed at the LETI. The candidate will also benefit from a collaboration with the instrument supplier.
Optical sensor development for in-situ and operando Li-ion battery monitoring
To improve the battery management system, it is required to have a better knowledge of the physical and chemical phenomena inside the cells. The next generation of cells will integrate sensors for deepest monitoring of the cell to improve the performances, safety, reliability and lifetime of the battery packs. The main challenge is thus to measure relevant physico-chemical parameters in the heart of the cell to get a direct access to the real state of the cell and thus to optimize its management. To address this challenge, a research project will start at CEA at the beginning of 2020 to develop innovative optical sensors for Li-ion battery monitoring. He / She will participate, in a first step, to the development of optical probes and their integration on optical fibres. The work will focus on the synthesis of a photo-chemical probe (nanoparticle and/or molecule) as active part of the sensor. Then, theses probes will be put on the optical fibre surface to form the sensor. The candidate will also participate to the realization of an optical bench dedicated to the testing of the sensors. In a second step, he / she will work on integrating the sensors into the Li-ion cells and test them in different conditions. The objective is to demonstrate the proof of concept: validation of the sensors efficiency to capture the behaviour of the cell and correlate it to electrochemical measurements.