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.
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.
New silicon-based alloys and composites for all-solid-state batteries: from combinatorial synthesis by magnetron sputtering to mechanosynthesis
All-solid state lithium-ion batteries using sulphide-based electrolytes are among the most studied at present in order to improve energy density, safety and fast charging. Although lithium metal was initially the preferred choice for the anode, the difficulties encountered in its implementation and the performance achieved suggest that alternatives should be proposed. Silicon offers an interesting compromise in terms of energy density and lifetime. However, it is necessary to look at anode materials developed for all-solid state batteries. To this end, we propose to collaborate with CEA Tech Nouvelle-Aquitaine, which has set up a combinatorial synthesis methodology using magnetron sputtering, in order to accelerate the identification of new compositions of silicon-based materials. Libraries of materials with compositions gradient in thin films will be prepared at CEA Tech Nouvelle-Aquitaine and then studied at CEA Grenoble. The most promising compositions will then be prepared by mechanosynthesis and characterised at CEA Grenoble. Significant work will be carried out on milling processes in order to optimise particle size and homogeneity, as well as structure and microstructure. Attention will also be paid to integration in all-solid state cells, drawing on the laboratory's expertise.
Design and reliability of modular architecture for reconfigurable and repairable PV panels
The integration of photovoltaic modules has become a challenge for adaptation to climate change, notably with the installation of specific PV modules in urban spaces, on vehicles or on agricultural farms. These modules are required to operate in more complex situations presenting high temporal variability and changing exposure to the sun. The scientific challenges of the project are to determine the conditions needed for optimizing the performance of PV modules regarding these external disturbances by the study of reconfigurable electrical module architectures. A reliability model will be developed to integrate the impact of the system architecture, in order to guarantee an improved level of reliability. In-depth work will be carried out on the entire PV module, from cell technologies to the final electrical characteristics requested, including electrical switching technologies. In a second phase, we will develop a design methodology in conjunction with a precise state of the art of available power switching technologies. The method will be applied to a use case responding primarily to the problem of shading and/or localised failure of the PV module. Finally, the proposed architectures will be evaluated by life cycle analysis. The designs authorizing maintenance or replacement of certain elements will be detailed and compared to the performance of usual modules.
Development of a ML-based analysis framework for fast characterization of nuclear waste containers by muon tomography
This PhD thesis focuses on developing an advanced analysis framework for inspecting nuclear waste containers using muon tomography, particularly the scattering method. Muon tomography, which leverages naturally occurring muons from cosmic rays to scan dense structures, has proven valuable in areas where traditional imaging methods fail. CEA/Irfu, with expertise in muon detectors, seeks to harness AI and Machine Learning (ML) to optimize muon data analysis, particularly to reduce long exposure times and improve image reliability.
The project will involve familiarizing with muography (muon tomography image) principles, simulating muon interactions with waste containers, and developing ML-based data augmentation and image processing techniques. The outcome should yield efficient tools to interpret muon images, enhance analysis speed, and classify container contents reliably. The thesis aims to improve nuclear waste inspection’s safety and reliability by producing cleaner, faster, and more interpretable muon tomography data through innovative analysis methods.
Theoretical studies of orbitronic and spin-orbit phenomena in heterostructures comprising van der Waals materials, metals and oxides
The proposed PhD thesis aims at finding the best-unexplored combinations of transition metals, oxides and 2D materials (transition metal dichalcogenides, 2D magnets, graphene…) to help optimizing and providing scientific underpinnings of next generation energy efficient spintronic storage and memory devices based on emerging fields of spin-orbitronics and orbitronics. The latter is a fascinating new field of research that exploits orbital currents and their interaction with spin currents mediated by spin-orbit coupling.
Namely, using first principles calculations combined with tight-binding approach and linear response theory, we will screen the potential of aforementioned heterostructures not only for spin-orbit phenomena such as Dzyaloshinskii-Moriya interaction (DMI), perpendicular magnetic anisotropy (PMA) and spin-charge interconversion based on Rashba and Rashba-Edelstein effects (REE), but also focus on Orbital Rashba Edelstein Effect (OREE). Furthermore, the mechanisms of control of these phenomena via external stimuli (strain, external electric and magnetic fields) will be investigated as well. These studies will help finding optimal material combination to tune DMI, PMA and spin-charge interconversion efficiency to help optimizing spintronic devices making thereby a significant contribution to the development of sustainable microelectronics.
The PhD will be based on a multiscale approach including ab initio, tight-binding and atomistic approaches thus highly motivated candidate with a good background in solid state physics, condensed matter theory and numerical simulations is required. He/she will perform his/her calculations on Spintec computational cluster nodes using first-principles packages based on density functional theory (DFT) combined with other simulation codes/tools. Results obtained will be carefully analyzed with the possibility of publication in international scientific journals. Strong collaboration with labs in France (CEA/LETI, Laboratoire Albert Fert (CNRS,Thales), Aix-Marseille Univ…) and abroad (ICN2-Barcelona, PGI Forschungszentrum Jülich, Osaka University) are previewed.
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.