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.
Plasma Etching development for the advanced nodes using SADP techniques
The miniaturization of the electronics components involves the development of new processes. Indeed, the 193nm immersion lithography alone does not permit anymore to achieve the dimensional requirements of the most advanced technological nodes (=10nm). Since the last 10 years, multi-patterning techniques have been developed to overcome the i193nm lithography limitations. Herein, we will study the « Self-Aligned Double Patterning » (SADP) technique that divides by two the initial pitch of the lithographical patterns. This technology relies on a conformal deposition of a dielectric film (spacer) over the initial patterns (mandrel). The spacers will be then used as a mask during the pattern transfer by plasma etching. The small targeted dimensions require a perfect control of the etching processes. However, the etching steps can damage the materials used herein leading to a dimension loss. One of the main challenge will be to control the etching steps and so the plasma-induced modification in order to satisfy the specifications (dimension, profile, material consumption, etch rate, uniformity…). Besides, the goal will be also to propose new SADP approaches allowing us to generate different type of patterns in order to produce planar FDSOI transistors, which is currently little reported in literature.
The challenges of this PhD ?
To develop innovative etching processes
To explore new couple of material (spacer/mandrel) and to propose an industrial integration flow that will be validated by electrical tests
To identify the technological obstacles and to propose solutions for overcoming them
To put in place a reliable characterization protocol in order to detect the physical and chemical modifications of the materials used and to accurately measure the final patterns’ dimensions
Sperm 3D - Male infertility diagnostic tool using holography for imaging and 3D tracking
Infertility is a growing problem in all developed countries. The standard methods for the diagnostic of male infertility examine the concentration, motility and morphological anomalies of individual sperm cells. However, one in five male infertility cases remain unexplained with the standard diagnostic tools.
In this thesis, we will explore the possibility to determine the male infertility causes from the detailed analysis of 3D trajectories and morphology of sperms swimming freely in the environment mimicking the conditions in the female reproductive tract. For this challenging task, we will develop a dedicated microscope based on holography for fast imaging and tracking of individual sperm cells. Along with classical numerical methods, we will use up-to date artificial intelligence algorithms for improving the imaging quality as well as for analysis of multi-dimensional data.
Throughout the project we will closely collaborate with medical research institute (CHU/IAB) specialized in Assisted Reproductive Technologies (ART). We will be examining real patient samples in order to develop a new tool for male infertility diagnosis.
Impact of plasma activation on reliability of Cu/SiO2 hybrid bonding integrations
In recent years, CEA-LETI emerged as a leading force in the development of advanced microelectronic manufacturing processes. A key focus has been on wafer-to-wafer Cu/SiO2 hybrid bonding (HB) process, an emerging technology increasingly employed for producing compact, high performance and multifunctional devices. Before bonding, a crucial surface activation step is necessary to enhance the mechanical strength of the assembled structures. Different approaches have been developed, and the most used in the industry is N2-plasma activation. However, this process remains controversial due to undesirable effects, the formation of Cu nodules at the bonding interface between particularly electrical pads and the passivation of Cu pads with chemical complexes. These issues can significantly compromise the electric properties and reliability of devices. In collaboration with STMicroelectronics and IM2NP, this PhD aims at studying the impact of plasma activation on Cu/SiO2 HB integrations.
Ultra-wide-field smart microscope for the detection of egg parasite (SCREENER)
In most parasitic cycles, the free phase passes through an egg stage, which is released by the host into the environment via a complex faecal matrix, which has highly variable and often low egg concentrations. The classical detection method relies on microscopic observation of these eggs, which implies a tedious and time-consuming preparation of the sample to concentrate the eggs, with highly variable sensitivity values. This detection is crucial because, once dispersed, the eggs contaminate the environment and food, leading to cases of parasitic zoonoses in humans.
Detection in environmental and food matrices is even more complex than for faeces because of the very low number of eggs present : 1 to 10 per sample in the vast majority of cases. The thesis aims at developing a lensless wide-field imaging system that will allow the counting and identification of parasite eggs in complex matrices, while increasing sensitivity. This will make it possible to automate detection, thus opening up the possibility of investigating more samples for better health surveillance.