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.
Microwave Near Field Sensing in Heterogeneous Media
This thesis focuses on the development of microwave near-field sensing techniques for applications in biomedicine, agronomy, and geophysics. The primary objective is to design low-complexity algorithms that effectively solve complex inverse problems related to the characterization and detection of dielectric properties with various geometric distributions in heterogeneous media.
The candidate will begin by conducting a comprehensive review of existing radar-based and advanced signal processing methods. A precise physical model of microwave propagation in near-field conditions will be developed, serving as the foundation for new detection methods based on the concept of physics-driven iterative tomography. The ultimate goal is to formulate efficient algorithms suitable for real-time applications and validate them through experimental implementation. To achieve this, an evolving prototype setup will be developed, progressing from 2D media to more complex 3D scenarios.
This interdisciplinary project combines physical modeling, algorithm development, and practical experimentation. It presents an opportunity to advance the field of microwave imaging, with significant implications for biomedical and environmental applications.
High-isolation power supply
With the rapid evolution of technologies and the growing challenges of miniaturization and resource management, power converters are facing ever more stringent performance requirements. To meet these needs, the use of wide-bandgap semiconductors such as SiC (silicon carbide) and GaN (gallium nitride) is becoming increasingly common. These materials significantly increase the switching speed of converters, reducing losses and improving efficiency.
However, this switching speed brings additional challenges: the steepness of the switching edges can cause stray currents that interfere with switch controls. To counter these undesirable effects, it is necessary to use switch drivers offering a high level of insulation. The traditional solution is based on high-frequency magnetic transformers, but these devices are expensive, take up a lot of space and offer limited insulation.
Thesis objective: the aim of this thesis is to design a new solution for powering wide-gap component drivers, by replacing magnetic transformers with piezoelectric transformers. This innovative approach aims to reduce costs, space requirements and improve the overall efficiency of power conversion systems.
Supervision and ressources: the selected candidate will work as part of a leading-edge research team, renowned for its expertise in the field of power conversion using piezoelectric resonators. The team has the resources and know-how to support the development and validation of this innovative technology.
Identification versus anonymisation from an embedded client operating on a blockchain
The first worldwide deployment of a blockchain dates back to 2010 with Bitcoin, which introduced a completely digital monetary system and a crypto-currency, bitcoin. Within Bitcoin, all transactions are publicly accessible and traceable, which should generate trust between stakeholders. However, the traceability of transactions, and ultimately of the crypto-currency, does not imply the traceability of users authenticated by an account address, or more precisely by a set of account addresses that are independent of each other. In this context, it can be complex to trace the individuals or legal entities owning the crypto-currency.
Crypto-currency is not the only use case supported by blockchain technology. The deployment of Ethereum in 2014, based on the use of smart contracts, opened up many other uses, in particular the protection of identifying data. In this area, the need for traceability versus furtivity can vary greatly from one use case to another. For example, on a blockchain that records the access of a worker owning an employment certificate to an industrial site, no information enabling the worker to be identified or his activity to be traced should appear. On the other hand, in the case of data collected by IoT sensors and processed by remote Edge devices, traceability of data and processing is desirable.
The thesis proposes to study different techniques for tracing digital assets on a blockchain, for stealthing their owners, and offering the possibility of auditing and identification by an authorised body. The aim is to build embedded devices, Edge or personal possibly embedding artificial intelligence, secured by hardware components, integrating different cryptographic solutions and account, data or identity wallet structures to meet the needs of the different use cases envisaged.
Advanced functions for monitoring power transistors (towards greater reliability and increased lifespan of power converters for energy)
In order to increase the power of electronic systems, a common approach is to parallelize components within modules. However, this parallelization is complicated by the dispersion of transistor parameters, both initial and post-aging. Fast switching of Wide Bandgap (WBG) semiconductors components often requires slowdowns to avoid over-oscillation and destruction.
An intelligent driving scheme, including adjusted control, control of internal parameters of circuits and devices, as well as a feedback loop, could improve reliability, service life and reduce the risk of breakage.
The objectives of the thesis will be to develop, study and analyze the performance of control and piloting functions of power components, in silicon carbide (SiC) or gallium nitride (GaN), which could ultimately be implemented in a dedicated integrated circuit (ASIC type).
This thesis subject aims to solve critical problems in the parallelization of power components, thus contributing to eco-innovation by increasing the lifespan of power modules.
The design of integrated circuits requires, at the end of the chain, circuit editing and failure analysis tools. One of these tools is the probing of electrical potential levels using an electron beam available in a SEM (Scanning Electron Microscope) to determine the electrical signal present in an area of the circuit, which may be a metal level or a transistor. This electronic probing technique was widely used in the 90s, and then partially abandoned despite a few recurrent publications on the technique. In recent years, this technique has been revived by using the backside of the component, probing via the silicon substrate and accessing the active areas of the component.
These debugging and failure analysis tools are also tools for attacking integrated circuits. This thesis topic falls within the scope of hardware cybersecurity and so-called invasive attacks. The PhD student will implement this electron beam probing technique on commercial SEMs and under conditions of use specific to cybersecurity. Various techniques will be considered to improve the probed signals and their use.
Power and data transmission via an acoustic link for closed metallic environments
This thesis focuses on the transmission of power and data through metal walls using acoustic waves. Ultimately, this technology will be used to power, read and control systems located in areas enclosed in metal, such as pressure vessels, ship hulls and submarines.
Because electromagnetic waves are absorbed by metal, acoustic waves are needed to communicate data or power through metal walls. These are generated by piezoelectric transducers bonded to either side of the wall. The acoustic waves are poorly attenuated by the metal, resulting in numerous reflections and multiple paths.
The aim of the thesis will be to develop a robust demonstrator of this technology, enabling the remote powering and communication of acoustic data through metal walls. This work will be based on advanced modelling of the acoustic channel in order to optimise the performance of the power and data transmission device. It will also involve developing innovative electronic building blocks to determine and maintain an optimum power transmission frequency, impacted by environmental conditions and typically by temperature.
The goal of this thesis will be the development and implementation of a communication system embedded in an FPGA and/or microcontroller in order to send sensor data through a metal wall of variable thickness. The limitations due to the imperfections of the channel and the electronics will lead to the invention of a large number of compensation methods and systems in the digital and/or analogue domain. Work will also have to be carried out on the choice of piezoelectric transducers and the characterisation of the channel, in conjunction with the acoustic wave activities of the laboratory working on the transmission of acoustic power.
Contact : nicolas.garraud@cea.fr and esteban.cabanillas@cea.fr
Integrity, availability and confidentiality of embedded AI in post-training stages
With a strong context of regulation of AI at the European scale, several requirements have been proposed for the "cybersecurity of AI" and more particularly to increase the security of complex modern AI systems. Indeed, we are experience an impressive development of large models (so-called “Foundation” models) that are deployed at large-scale to be adapted to specific tasks in a wide variety of platforms and devices. Today, models are optimized to be deployed and even fine-tuned in constrained platforms (memory, energy, latency) such as smartphones and many connected devices (home, health, industry…).
However, considering the security of such AI systems is a complex process with multiple attack vectors against their integrity (fool predictions), availability (crash performance, add latency) and confidentiality (reverse engineering, privacy leakage).
In the past decade, the Adversarial Machine Learning and privacy-preserving machine learning communities have reached important milestones by characterizing attacks and proposing defense schemes. Essentially, these threats are focused on the training and the inference stages. However, new threats surface related to the use of pre-trained models, their unsecure deployment as well as their adaptation (fine-tuning).
Moreover, additional security issues concern the fact that the deployment and adaptation stages could be “on-device” processes, for instance with cross-device federated learning. In that context, models are compressed and optimized with state-of-the-art techniques (e.g., quantization, pruning, Low Rank Adaptation) for which their influence on the security needs to be assessed.
The objectives are:
(1) Propose threat models and risk analysis related to critical steps, typically model deployment and continuous training for the deployment and adaptation of large foundation models on embedded systems (e.g., advanced microcontroller with HW accelerator, SoC).
(2) Demonstrate and characterize attacks, with a focus on model-based poisoning.
(3) Propose and develop protection schemes and sound evaluation protocols.
Impact of the Pulse Width Modulation strategy on the semiconductor ageing
The Pulse Witdh Modulation strategy (PWM) is a fundamental technique in power electronics. It is used to control the Energy transfer by modifying the pulse width of the control signals in a power converter. In an automotive traction inverter, this PWM strategy applied to a transistor phase leg allows to convert the DC current from the battery to an AC current adapted to the motor windings. The impact of the PWM on the performances and the reliability of the engine have been widely studied in the litterature. However, the impact of the PWM strategy on the reliability and the ageing of the semiconductor devices inside the power modules has not been adressed. This is particularly true for the power modules intagrating wide bandgap semiconductors (eg: SiC) which are widely used for 10 years. The main objective of this thesis is to understand and model the impact of several PWM strategies on the ageing of SiC power semiconductor devices.
The thesis targets to define a link between the stress on the semicondcutor devices and the shift of its key parameters offering the possibility to define a PWM strategy able to maximize the long term performances and the lifetime of the power electronics system. By combining experimental and theroretical approaches, this thesis will contribute to improve the PWM strategies in power electronics systems.