Multi-scale modeling of the electromagnetic quantum dot environment
In the near future, emerging quantum information technologies are expected to lead to global breakthroughs in high performance computing and secure communication. Among semiconductor approaches, silicon-based spin quantum bits (qubits) are promising thanks to their compactness featuring long coherence time, high fidelity and fast qubit rotation [Maurand2016], [Meunier2019]. A main challenge is now to achieve individual qubit control inside qubit arrays.
Qubit array constitutes a compact open system, where each qubit cannot be considered as isolated since it depends on the neighboring qubit placement, their interconnection network and the back-end-line stack. The main goal of this post-doctoral position is to develop various implementation of spin control on 2D qubit array using multi-scale electromagnetic (EM) simulation ranging from nanometric single qubit up to millimetric interconnect network.
The candidate will i) characterize radio-frequency (RF) test structures at cryogenic temperature using state-of-the-art equipment and compare results with dedicated EM simulations, ii) evaluate the efficiency of spin control and allow multi-scale optimization from single to qubit arrays [Niquet2020], iii) integrate RF spin microwave control for 2D qubit array using CEA-LETI silicon technologies.
The candidate need to have a good RF and microelectronic background and experience in EM simulation, and/or design of RF test structures and RF characterization. This work takes place in a dynamic tripartite collaborative project between CEA-LETI, CEA-IRIG and CNRS-Institut Néel (ERC “Qucube”).
Postdoctoral fellow in AI, real time signal processing and software for real time epilepsy prediction/forecasting for closed loop neuromodulation by focal Cooling.
To date seizure suppression stimulation technologies (electrical stimulation) are majorly based on seizure detection procedure. No study has provided sound evidence that prospective seizure prediction/forecasting can be used to trigger closed loop therapeutics for drug resistant epilepsy treatment. Our proposal is based on the existing motor brain-computer interface algorithms already in clinical use. They can be adapted to generate prediction/forecasting of seizures occurrence. Our working hypothesis is that treating during high-risk seizures periods and not during the actual seizure would require relatively minor doses of the therapeutical element. This will reduce the power consumption and open the door to fully implantable system. Decoding algorithms will be potentially redesigned to respond better to the epileptic seizures forecasting task. They will be compared to the state of the art CNN based approaches, and other approaches. Prediction/forecasting seizures algorithms will be evaluated in an epilepsy model established at Clinatec, using non-human primates, and the algorithms will be refined over time. Cooling the epileptic foci is an effective way to stop de seizure before generalization. This model allows us to test the efficacy of the algorithms in treating focal seizures. An assessment of hardware embedding design constraints would be conducted to facilitate next steps for the clinical device development. The project will benefit from a collaboration between Clinatec and DSYS/SSCE; and will be in line with upcoming activities of LETI’s artificial intelligence platform.
Fast-scintillator-based device for on-line FLASH-beam dosimetry
New cancer treatment modalities aim to improve the dose delivered to the tumor while sparing healthy tissue as much as possible. Various approaches are being developed, including the temporal optimization of the dose delivered with very high dose rate irradiation (FLASH).
In this particular case, recent studies have shown that FLASH irradiation with electrons was as effective as photon beam treatments for tumor destruction while being less harmful to healthy tissue. For these beams, the instantaneous doses are up to several orders of magnitude higher than those produced by conventional beams. Conventional active dosimeters saturate under irradiation conditions at very high dose rates per pulse, therefore on-line dosimetry of the beam is not possible.
We propose to develop a dosimeter dedicated to the measurement of beams in FLASH radiotherapy based on an ultra-fast plastic scintillator coupled with a silicon photomultiplier sensor (SiPM). The novelty of the project lies both in the chemical composition of the plastic scintillator which will be chosen for its response time and its wavelength emission to have a response adapted to the impulse characteristics of the beam, and in the final sensor with the possibility of coupling the plastic scintillator to a miniaturized SiPM matrix.
The final goal is to be able to access, with a reliable methodology, the dosimetry and in-line geometry of FLASH beams.
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.
Dynamic monitoring by light scattering of mass transfer between two phases in multiphase flows
The understanding and the modeling of recycling processes studied at CEA, require the measurement of both local and average properties of multiphase flows involved in chemical engineering devices. Moreover, as the R&D studies are generally conducted on small-scale experiments, access to these quantities is often difficult, especially considering that measurement methods should not disturb the observed system. In this context, optical methods, associated to extensive and rigorous physical simulations of light/particles interactions, are particularly relevant and, accordingly under specific developments since several years. Therefore, the DMRC/LGCI (CEA Marcoule), in collaboration with the laboratory IUSTI (CNRS and Aix-Marseille University), develops two optical interferometric techniques suitable for R&D studies: the Digital In-line Holography (DIH) and the Rainbow Refractometry (RR). Previous works have shown that DIH allows a simultaneous measurement of 3D-positions, shape and size of flowing particles, even considering astigmatic geometries, while RR gives access to the size and refractive index of each particle or of set of particles, which considering linear optics is directly linked to their composition. This study aims to go further in multiphase flows characterization with these two technics by following three main objectives: 1) propose original solutions for the characterization of materiel compositions thanks to DIH, 2) deepen inverse methods in RR to allow the study of clouds of particles with variable compositions and to take into account gradients of concentration around a sessile drop, 3) evaluate the relevance of these technics for lab on chip systems.