IMPROVING OPTICALLY PUMPED MAGNETOMETERS FOR BIOMEDICAL IMAGING
Our lab works on optically pumped magnetometers (OPM) based on helium-4 metastable atoms. Our main achievement in last years has been the design and space qualification of the most advanced OPMs available for spatial exploration, which were launched on ESA Swarm mission [1]. With this very same species we have developed OPMs for medical imaging of brain (MEG) and heart (MCG), which have the advantage of operating at room temperature. The development of these two imaging techniques is an opportunity to better understand and diagnose pathologies like epilepsy, Alzheimer or arrhythmia.
A few years ago we performed proof of concept measurements of both MCG and MEG with primitive versions of our sensors [2,3]. After getting a better understanding of our sensors physics [4] and implementing substantial improvements, we are now developing arrays of OPMs and collaborating with several clinical teams in order to test them for different applications and environments. The purpose of this post-doctoral position is to contribute to the development of magnetometer arrays. It involves experimental work to improve the current prototypes of medical OPM arrays: the post-doc will be notably in charge of improving the intrinsic noise of the sensor and identifying the best way to build robust, reproducible architectures that could be replicated in arrays of several hundreds of sensors.
This work is aimed at bringing this technology to the medical imaging market, in collaboration with a start-up currently prepared by CEA-Leti. It will be carried out in a multidisciplinary team, composed of researchers, experienced engineers, PhD students and post-docs, specialized in the fields of optics, lasers, magnetism and electronics. It will also rely on collaborations with medical research teams in neurology and cardiology.
[1] http://smsc.cnes.fr/SWARM
[2] S. Morales et al.,
[3] E. Labyt et al., IEEE Transactions on Medical Imaging (2019).
[4] F. Beato et al. Physical Review A (2018)
LAB AND FIELD WORK ON OPTICALLY PUMPED MAGNETOMETERS
Our lab works on optically pumped magnetometers (OPM) based on helium-4 metastable atoms. Our main achievement in last years has been the design and space qualification of the most advanced OPMs available for spatial exploration, launched on ESA Swarm mission [1].
With this same species we have developed OPMs for medical imaging of brain (MEG) and heart (MCG), which have the advantage of operating at room temperature, with no heating or cooling.
The development of these two imaging techniques is an opportunity to better understand and diagnose pathologies like epilepsy, Alzheimer or arrhythmia.
A few years ago we performed proof of concept measurements of both MCG and MEG with primitive versions of our sensors [2,3]. After getting a better understanding of our sensors physics [4] and implementing substantial improvements, we are now developing arrays of OPMs and collaborating with several clinical teams in order to test them for different applications and environments.
The purpose of this post-doctoral position is to contribute to the development of magnetometer arrays. It involves mainly the deployment of OPM arrays in the clinical environments where they are going to be tested by several of our partner medical research teams in both neurology and cardiology. The post-doc should be able to deploy and operate the sensors in these environments, solve the practical issues, and bring feedback on all kind of improvements that are needed. He or she will also participate in the implementation of some of these improvements, and their tests in lab environment.
This work is aimed at bringing this technology to the medical imaging market. It will be carried out in a multidisciplinary team, composed of researchers and experienced engineers.
[1] http://smsc.cnes.fr/SWARM
[2] S. Morales et al., Phys. Med. B
[3] E. Labyt et al., IEEE Transactions on Medical Imaging (2019).
[4] F. Beato et al. Physical Review A (2018)
Application of a MDE approach to AI-based planning for robotic and autonomous systems
The complexity of robotics and autonomous systems (RAS) can only be managed with well-designed software architectures and integrated tool chains that support the entire development process. Model-driven engineering (MDE) is an approach that allows RAS developers to shift their focus from implementation to the domain knowledge space and to promote efficiency, flexibility and separation of concerns for different development stakeholders. One key goal of MDE approaches is to be integrated with available development infrastructures from the RAS community, such as ROS middleware, ROSPlan for task planning, BehaviorTree.CPP for execution and monitoring of robotics tasks and Gazebo for simulation.
The goal of this post-doc is to investigate and develop modular, compositional and predictable software architectures and interoperable design tools based on models, rather than code-centric approaches. The work must be performed in the context of European projects such as RobMoSys (www.robmosys.eu), and other initiatives on AI-based task planning and task execution for robotics and autonomous systems. The main industrial goal is to simplify the effort of RAS engineers and thus allowing the development of more advanced, more complex autonomous systems at an affordable cost. In order to do so, the postdoctoral fellow will contribute to set-up and consolidate a vibrant ecosystem, tool-chain and community that will provide and integrate model-based design, planning and simulation, safety assessment and formal validation and verification capabilities.
Simulation and electrical characterization of an innovative logic/memory CUBE for In-Memory-Computing
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).
However, today’s existing memory technologies are ineffective to In-Memory compute billions of data items. Things will change with the emergence of three key enabling technologies, under development at CEA-LETI: non-volatile resistive memory, new energy-efficient nanowire transistors and 3D-monolithic integration. At LETI, we will leverage the aforementioned emerging technologies towards a functionality-enhanced system with a tight entangling of logic and memory.
The post-doc will perform electrical characterizations of CMOS transistors and Resistive RAMs in order to calibrate models and run TCAD/spice simulations to drive the technology developments and enable the circuit designs.
Detection of cyber-attacks in a smart multi-sensor embedded system for soil monitoring
The post-doc is concerned with the application of machine learning methods to detect potential cyber-security attacks on a connected multi-sensor system. The application domain is the agriculture, where CEA Leti has several projects, among which the H2020 project SARMENTI (Smart multi-sensor embedded and secure system for soil nutrient and gaseous emission monitoring). The objective of SARMENTI is to develop and validate a secure, low power multisensor systems connected to the cloud to make in situ soil nutrients analysis and to provide decision support to the farmers by monitoring soil fertility in real-time. Within this topic, the postdoc is concerned with the cyber-security analysis to determine main risks in our multi-sensor case and with the investigation of a attack detection module. The underlying detection algorithm will be based on anomaly detection, e.g., one-class classifier. The work has tree parts, implement the probes that monitor selected events, the communication infrastructure that connects the probes with the detector, and the detector itself.
Compressed Sensing Electron Tomography: Quantitative Multi-dimensional Characterization of Nanomaterials
Electron tomography (ET) is a well-established technique for the 3D morphological characterization at the nanoscale. ET applied to spectroscopic modes for 3D structural and chemical analysis has become a hot topic but necessitates long exposure times and high beam currents. In this project, we will explore advanced compressed sensing (CS) approaches in order to improve the resolution of spectroscopic ET and reduce significantly the dose. More precisely, we will focus on the following two tasks: 1. Comparison of total variation minimization, orthogonal or undecimated wavelets, 3D curvelets or ridgelets and shearlets for nano-objects with different structures/textures; 2. Comparison of PCA and novel CS-inspired methods such as sparse PCA for dimensionality reduction and spectral un-mixing. The code will be written in Python, using Hyperspy (hyperspy.org) and PySAP (https://github.com/CEA-COSMIC/pysap) libraries.
The project follows a multidisciplinary approach that involves the strong expertise of the coordinator in ET and the input of two collaborators with complementary skills: Philippe Ciuciu with expertise in MRI (DRF/Joliot/NEUROSPIN/Parietal) and Jean-Luc Starck with expertise in cosmology, signal processing and applied maths (DRF/IRFU/DAP/CosmoStat). The three communities share a strong interest in compressed sensing algorithms.
Development of a cell analysis algorithm for phase microscopy imaging
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 is to extend the analysis of the datasets produced by lens-free video microscopy. The objective is to study a real-time cell tracking algorithm to follow every single cell and to plot different cell fate events as a function of time. To this aim, researches will be carried on segmentation and tracking algorithms that should outperform today’s state-of-the-art methodology in the field. In particular, the algorithms should yield good performances in terms of biological measures and practical usability. This will allow us to outperform today’s state-of-the-art methodology which are optimized for the intrinsic performances of the cell tracking and cell segmentation algorithms but fails at extracting important biological features (cell cycle duration, cell lineages, etc.). To this aim the recruited person should be able to develop a method that either take prior information into account using learning strategies (single vector machine, deep learning, etc.) or analyze cells in a global spatiotemporal video. We are looking people who have completed a PhD in image processing, with skills in the field of microscopy applied to biology.
FDSOI technology scaling beyond 10nm node
FDSOI (Fully-Depleted Silicon On Insulator) is acknowledged as a promising technology to meet the requirements of emerging mobile, Internet Of Things (IOT), and RF applications for scaled technological nodes [1]. Leti is a pioneer in FDSOI technology, enabling innovative solutions to support industrial partners.
Scaling of FDSOI technology beyond 10nm node offers solid perspectives in terms of SoC and RF technologies improvement. Though from a technological point of view, it becomes challenging because of thin channel thickness scaling limitation around 5nm to maintain both good mobility and variability. Thus, introduction of innovative technological boosters such as strain modules, alternative gate process, parasitics optimization, according to design rules and applications, become mandatory [2].
The viability of these new concepts should be validated first by TCAD simulations and then implemented on our 300mm FDSOI platform.
This subject is in line with the recent LETI strategy announcement and investments to develop new technological prototypes for innovative technology beyond 28nm [3].
The candidate will be in charge to perform TCAD simulations, to define experiment and to manage them until the electrical characterization. The TCAD simulations will be performed in close collaboration with the TCAD team. The integration will be done in the LETI clean room in collaboration with the process and integration team. Candidate with out-of-the-BOX thinking, autonomy, and ability to work in team is mandatory.
[1] 22nm FDSOI technology for emerging mobile, Internet-of-Things, and RF applications, R. Carter et al, IEEE IEDM 2016.
[2] UTBB FDSOI scaling enablers for the 10nm node, L. Grenouillet et al, IEEE S3S 2013.
[3]https://www.usinenouvelle.com/article/le-leti-investit-120-millions-d-euros-dans-sa-salle-blanche-pour-preparer-les-prochaines-innovations-dans-les-puces.
AlGaN/GaN HEMTs transfert for enhanced electrical and thermal performances
Due to their large critical electric field and high electron mobility, gallium nitride (GaN) based devices emerge as credible candidates for power electronic applications. In order to face the large market needs and benefit from available silicon manufacturing facilities, the current trend is to fabricate those devices, such as aluminum gallium nitride (AlGaN)/GaN high electron mobility transistors (HEMTs), directly on (111) silicon substrates. However, this pursuit of economic sustainability negatively affects device performances mainly because of self-heating effect inherent to silicon substrate use. New substrates with better thermal properties than silicon are desirable to improve thermal dissipation and enlarge the operating range at high performance.
A Ph.D. student in the lab. has developed a method to replace the original silicon material with copper, starting from AlGaN/GaN HEMTs fabricated on silicon substrates. He has demonstrated the interest of the postponement of a GaN power HEMT on a copper metal base with respect to self heating without degrading the voltage resistance of the component. But there are still many points to study to improve the power components.
Post-doc objectives : We propose to understand what is the best integration to eliminate self-heating and increase the voltage resistance of the initial AlGaN/GaN HEMT. The impact of the component transfer on the quality of the 2D gas will be analyzed.
The same approach can be made if necessary on RF components.
Different stacks will be made by the post-doc and he will be in charge of the electrical and thermal characterizations. Understanding the role of each part of the structure will be critical in choosing the final stack.
This process will also be brought in larger dimensions.
This post-doc will work if necessary in collaboration with different thesis students on power components.
Tunnel Junction for UV LEDs: characterization and optimization
Besides existing UV lamps, UV LEDs emitting in the UV-C region (around 265 nm) are considered as the next solutions for cost efficient water sterilization systems. But existing UV-C LEDs based on AlGaN wide band gap materials and related quantum well heterostructures still have low efficiencies which precludes their widespread use in industrial systems.
Analysing the reasons of the low efficiencies of present UV-C LEDs led us to propose a solution based on the use of a Tunnel Junction (TJ) inserted within the AlGaN heterostructure diode. p+/ n+ tunnel junctions are smart solutions to cope with doping related problems in the wide band gap AlGaN materials but give rise to extra tunneling resistances that need to be coped with. The post-doctoral work is dedicated to understanding the physics of tunneling processes in the TJ itself for a better control of the tunneling current.
The post-doctoral work will be carried out at the “Plate-Forme de Nanocaractérization” in CEA/ Grenoble, using different optical, structural and electrical measurements on stand-alone TJs or on TJs inserted within LEDs. The candidate will have to interact strongly with the team in CNRS/CRHEA in Sophia Antipolis where epitaxial growth of the diodes will be undertaken. The work is part of a collaborative project named "DUVET" financed by the Agence Nationale de la Recherche (ANR).