Development of multiplexed photon sources for quantum technologies
Quantum information technologies offers several promises in domains such as computation or secured communications. There is a wide variety of technologies available, including photonic qubits. The latter are robust against decoherence and are particularly interesting for quantum communications applications, even at room temperature. They also offers an alternative to other qubits technologies for quantum computing. For the large-scale deployment of those applications, it is necessary to have cheap, compact and scalable devices. To reach this goal, silicon photonics platform is attractive. It allows implementing key components such as generation, manipulation and detection of photonic qubits.
Solid-state photon generation may occur with different physical processes. Among those, the non-linear photon pair generation has several benefits, such as working at room temperature, the ability to generate heralded single photon, or entangled photon pairs…
You will work on multiplexed parametric photon pair sources in order to surpass the inherent limits of the physical process for generating photon pairs. This will include the development, the fabrication monitoring, and the characterization in the laboratory. In the goal of a full integration on chip, it is necessary to be able to filter effectively unwanted light, in order to keep only photons of interest.
MOCVD growth of 2D ferroelectric In2Se3 films for high density, low consumption nonvolatile memories
Room temperature ferroelectric thin films are the key element of high density, low consumption nonvolatile memories. However, with the further miniaturization of the electronics devices beyond the Moore’s law, conventional ferroelectrics suffer great challenge arising from the critical thickness effect, where the ferroelectricity is unstable if the film thickness is reduced to nanometer or single atomic layer limit. Two-dimensional (2D) materials, thanks to their stable layered structure, saturate interfacial chemistry, weak interlayer couplings, and the benefit of preparing stable ultra-thin film at 2D limit, are promising for exploring 2D ferroelectricity and related device applications. So far, proof of concept demonstrating 2D ferroelectricity has predominantly utilized small flakes (less than a few hundred µm) mechanically exfoliated from a bulk crystal. In particular, atomically thin alpha (or gamma)-In2Se3 lamellar semiconductor preserves a ferroelectric character at 2D limit.
Given the imperative for wafer-scale electronics applications, there is a pressing need for large area growth of high quality 2D materials using bottom-up processes. The objective of this PhD project is to develop the growth of lamellar In2Se3 in its alpha or gamma phase crystal structures by chemical vapor phase epitaxy (MOCVD) on large silicon substrates (200 mm). The proof of concept of a ferroelectric memory cell will be performed by directly depositing a metal electrode on the surface of the 2D ferroelectric material without damaging it.
3D chemical analysis of downscaled ePCM devices for sub-18 nm technology nodes using STEM-EDX tomography and machine learning tools
The context of this PhD is the recent progress of Phase-Change Memory technology in the embedded applications (ePCM). The ultimate scaling of ePCM for sub-18nm nodes poses many challenges not only in fabrication, but also in the physico-chemical characterization of these devices. The aim of the project is to study the 3D chemical segregation/crystallization phenomena in new PCM alloys integrated into planar and vertical ePCM scaled devices, using electron tomography in STEM-EDX (and 4D-STEM) mode. Given the extreme downscaling and the complex geometry of the devices, the focus will be on optimizing experimental conditions and applying machine learning and deep learning techniques to improve the quality and reliability of the obtained 3D results. A correlation with the device electrical behavior will be carried out to better understand the phenomena behind failures after endurance and after data loss at high temperatures.
A probe-corrected Cold-FEG NeoARM TEM (60kV-200kV) will be used for the tomographic data acquisition. It is equipped with two large solid angle SSD detectors (JEOL Centurio), a CEOS Energy-Filtering and Imaging Device (CEFID) and a Timepix3 direct electron camera. The candidate will also have access to in-house Python codes as well as to the computing resources needed to carry out the spectral and tomographic data analysis.
CORTEX: Container Orchestration for Real-Time, Embedded/edge, miXed-critical applications
This PhD proposal will develop a container orchestration scheme for real-time applications, deployed on a continuum of heterogeneous computing resources in the embedded-edge-cloud space, with a specific focus on applications that require real-time guarantees.
Applications, from autonomous vehicles, environment monitoring, or industrial automation, applications traditionally require high predictability with real-time guarantees, but they increasingly ask for more runtime flexibility as well as a minimization of their overall environmental footprint.
For these applications, a novel adaptive runtime strategy is required that can optimize dynamically at runtime the deployment of software payloads on hardware nodes, with a mixed-critical objective that combines real-time guarantees with the minimization of the environmental footprint.
TeraHertz Landau emission in HgTe/CdTe topological quantum wells
Quantum well heterostructures of HgTe/CdTe are known as topological insulators. They inherit very peculiar electronic properties. One of them is the ability of producing TeraHertz emission from inter-Landau energy level optical transitions. These transitions can be envisioned to lead to coherent optical sources in spectral range where they are basically absent. The PhD Thesis consists in elaborating and characterizing HgTe/CdTe multiple quantum well structures by epitaxy, process them in order to add functionality through optical cavities metallic report or deposition and electrical gating, and finally carry out full range optical spectroscopy of Landau emission in magnetic fields. The PhD will be carried out in a collaborative environment between CEA-Leti and Institut Néel (CNRS) in Grenoble, France, two leading laboratories in the expertise in material growth and Physics of HgTe/CdTe topological insulator systems. The results will help to understand the potential of application of this peculiar material system in TeraHertz laser sources and hopefully lead to the first demonstration of spontaneous emission in the TeraHerz range.
Wideband Hybrid Transmitter for Future Wireless Systems
This research is part of an effort to reduce the energy consumption and carbon footprint of future wireless systems by investigating innovative transmitter (TX) architectures with improved energy efficiency. Objective of the thesis is to elaborate a novel TX architecture for beyond 5G and 6G standards. Efficiency enhancement design techniques such as supply modulation or load modulation have been proposed in the past to improve TX efficiency, but the increasing requirement in terms of instantaneous bandwidth tends to limit the benefit of those techniques. During the thesis, the candidate will develop a novel integrated hybrid TX architecture that combine load and supply modulation. On particular, she/he will develop a dedicated co-design methodology between the power amplifier and the supply modulator in order to address 6G-FR3 bands (10GHz+) with high PAPR (>10dB) and high bandwidth (>200MHz) signals.
The candidate will join the integrated radiofrequency architecture laboratory where various skill (system, IC design and layout …) and field of expertise are represented (RF power, Low power RF, RF sensors, High-speed mmW). During the thesis, she/he will analyze and model new TX architectures, perform IC and package design, including layout, to achieve and validate hardware demonstrators.
link:
http://www.leti-cea.com/cea-tech/leti/english/Pages/Applied-Research/Facilities/Integration-Platform.aspx
https://www.youtube.com/watch?v=da3x89qxCHM
We are looking for this type of profile:
• MSc or Engineering degree in electronics or microelectronics
• Knowledge in transistor technology (CMOS, Bipolar, GaN…) and Analog/RF design
• Experience in ADS or/and Cadence
• Basic programming skills (Python, Matlab …)
• First experience in IC design is an asset
Contact: Guillaume.robe@cea.fr, Pascal.reynier@cea.fr
Key words : Power amplifier, Load modulation, Supply modulator, RF module.
New rapid diagnostic tool for sepsis: microfluidic biochip for multi-target detection by isothermal amplification
Sepsis is among the main cause of death across the world, and is caused by severe bacterial infection but can also originate from viruses, fungi or even parasites. In order to drastically increase survival rates, a rapid diagnostic and appropriate treatment is of paramount importance. The commercially available tools for nucleic acid detection by qPCR are able to sense multiple targets. However, these multiplexed analyses arise from the accumulation of analysis channels or reaction chambers where only one target can be detected. The original sample has to be divided, resulting in a loss of sensibility since a smaller amount of targets is available in channels or chambers.
In order to tackle the question of “How to detect multiple targets without a loss in sensibility?”, the PhD candidate will have to develop a multiplexed detection in a single reaction chamber by localized immobilization of LAMP primers (Loop-mediated isothermal amplification) on a solid substrate like COC or glass.
The expected outcome is a biochip allowing for real-time and fast (minutes) detection of several molecular DNA targets including: primers design and selection, primers immobilization on surface, integration of the biochip into a microfluidic cartridge and data collection and management for fluorescence detection of targets.
This innovative work will provide the PhD candidate with strong skills in diverse scientific domains such as molecular biology, surface functionalization, modelling and simulation, in a very multidisciplinary working environment.
Foundations of Semantic Reasoning for Enhanced AI Cooperation in 6G Multi-Agent Communications
6G will integrate 5G and AI to merge physical, cyber and sapience spaces, transforming network interactions, revolutioning AI-driven decision-making and automation and radically changing the overall system’s perception of the foundational concepts of information and reliability. This requires the native-by-design integration of AI and communication system. Current 5G technologies cannot support such change. 5G limits data to be “teleported blindly” along the network without a priori understanding of how informative is for the receiver(s). As a result, AI algorithm outcomes remain limited to sophisticated pattern recognition and statistical correlations. This represent a major limitation of today sense-process-communicate-memorize intelligent information systems.
To support such revolution with AI, the emerging concept of semantic and goal-oriented communications transforms how information is processed by enabling AI to selectively collect, share, and process data based on its relevance, value, or timeliness to the receiver. Unlike 5G’s focus on high-capacity data transport, semantic communications prioritize meaningful, compressed knowledge sharing to enhance AI reasoning, adapt to diverse environments, and surpass current limitations in intelligent decision-making.
This PhD research explores three cutting-edge areas: (1) semantic communication, where today state of the art mostly is focused on AI-driven semantic compression and robustness, (2) integrated communication and sensing, merging data exchange and environmental sensing for resource-efficient applications, and (3) advances in compositional learning and AI reasoning, enabling intelligent systems to process complex, multi-modal data.
This research is focused on the development of abstract concept compositionality models that AI agents can utilize to understand and reason over complex semantic structures. In this context, the PhD candidate will design new methodologies for compositional reasoning that align with the requirements of multi-user, goal-oriented communication. The models will be constructed to enable compositional information exchange where AI agents can intuitively form, exchange, and infer based on compound semantic representations. By focusing on the inherent compositionality and adaptability of semantic exchanges, this research is positioned to support the next generation of intelligent, contextually aware communication systems. These systems will allow for a more precise and meaningful exchange of information between AI agents, enhancing their decision-making and cooperative abilities across a range of applications, from autonomous robotic swarms to networked IoT devices in smart cities and other intelligent environments. The PhD research will benchmark the proposed novel theoretical grounded concepts against current state of the art solutions in semantic communications by numerical simulation.
In-Sensor Computing for MEMS Sensors: Toward an Electromechanical Neural Network
The rise of machine learning models for processing sensor data has led to the development of Edge-AI, which aims to perform these data processing tasks locally, directly at the sensor level. This approach reduces the amount of data transmitted and eases the load on centralized computing centers, providing a solution to decrease the overall energy consumption of systems. In this context, the concept of in-sensor computing has emerged, integrating data acquisition and processing within the sensor itself. By leveraging the physical properties of sensors and alternative computing paradigms, such as reservoir computing and neuromorphic computing, in-sensor computing eliminates the energy-intensive steps of signal conversion and processing.
Applying this concept to MEMS sensors enables the processing of signals such as acceleration, strain, or acoustic signals, with a significant reduction, or even elimination, of traditional electronic components. This has rekindled interest in mechanical computing devices and their integration into MEMS sensors like microphones and accelerometers. Recent research explores innovative MEMS devices integrating recurrent neural networks or reservoir computing, showing promising potential for energy efficiency. However, these advancements are still limited to proof-of-concept demonstrations for simple classification tasks with a very low number of neurons.
Building on our expertise in MEMS-based computing, this doctoral work aims to push these concepts further by developing a MEMS device that integrates a reprogrammable neural network with learning capabilities. The objective is to design an intelligent sensor that combines detection and preprocessing on a single chip, optimized to operate with extremely low energy consumption, in the femtoJoule range per activation. This thesis will focus on the design, fabrication, and validation of this new device, targeting low-frequency signal processing applications in high-temperature environments, paving the way for a new generation of intelligent and autonomous sensors.
On-line monitoring of bioproduction processes using 3D holographic imaging
The culture of adherent cells on microcarriers (MCs) is a promising approach for various bioproduction applications, such as drug manufacturing and delivery, regenerative medicine, and tracking of cellular differentiation. However, the analysis of single cell morphology and behavior without affecting the substrate integrity remains a major challenge. Lens-free holographic imaging is emerging as a promising solution for real-time, non-invasive monitoring of cellular processes. This technique captures wide field of view images without requiring exogenous labeling or sample manipulation, thus preserving the integrity of the cellular environment.
This thesis proposes the development of a 3D lens-free imaging system to monitor cells on MCs in near real-time. The microscope will be coupled with advanced algorithms for data reconstruction and analysis in on-line bioreactors. The use of deep learning techniques will allow for real-time segmentation and analysis of single cells, facilitating the tracking of cellular dynamics. This innovative project paves the way to a non-invasive monitoring of 3D multicellular samples, with potential applications on organ-on-chip and more complex organoids systems.