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.
Hardware-aware Optimizations for Efficient Generative AI with Mamba Networks
Generative AI has the potential to transform various industries. However, current state-of-the-art models like transformers face significant challenges in computational and memory efficiency, especially when deployed on resource-constrained hardware. This PhD research aims to address these limitations by optimizing Mamba networks for hardware-aware applications. Mamba networks offer a promising alternative by reducing the quadratic complexity of self-attention mechanisms through innovative architectural choices. By leveraging techniques such as sparse attention patterns and efficient parameter sharing, Mamba networks can generate high-quality data with significantly lower resource demands. The research will focus on implementing hardware-aware optimizations to enhance the efficiency of Mamba networks, making them suitable for real-time applications and edge devices. This includes optimizing training and inference times, as well as exploring potential hardware accelerations. The goal is to advance the practical deployment of generative AI in resource-constrained domains, contributing to its broader adoption and impact.
Exploration of unsupervised approaches for modeling the environment from RADAR data
Radar technologies have gained significant interest in recent years, particularly with the emergence of MIMO radars and "Imaging Radars 4D". This new generation of radar offers both opportunities and challenges for the development of perception algorithms. Traditional algorithms such as FFT, CFAR, and DOA are effective for detecting moving targets, but the generated point clouds are still too sparse for precise environment model. This is a critical issue for autonomous vehicles and robotics.
This thesis proposes to explore unsupervised Machine Learning techniques to improve environment model from radar data. The objective is to produce a richer model of the environment to enhance data density and scene description, while controlling computational costs for real-time computing. The thesis will address the question of which types of radar data are best suited as inputs for algorithms and for representing the environment. The candidate will need to explore non-supervised algorithmic solutions and seek computational optimizations to make these solutions compatible with real-time execution.
Ultimately, these solutions must be designed to be embedded as close as possible to the sensor, allowing them to be executed on constrained targets.
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.
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.
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.
Securing Against Side-Channel Attacks by Combining Lightweight Software Countermeasures
Side-channel attacks, such as analyzing a processor's electrical consumption or electromagnetic emissions, allow for the recovery of sensitive information, including cryptographic keys. These attacks are particularly effective and pose a serious threat to the security of embedded systems.
This thesis focuses on combining low-impact software countermeasures to strengthen security against side-channel attacks, an idea that remains poorly explored in the current state of the art. The goal is to identify synergies and incompatibilities between these countermeasures to create more effective and lightweight solutions. In particular, low-entropy masking countermeasures will be considered.
These ideas will be applied on cryptography algorithm, with a particular focus on post-quantum cryptography algorithms.
The thesis aims to develop new ways to secure software, offering better trade-offs between security and performance than existing approaches.
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.