Optimisation of advanced mask design for sub-micrometer 3D lithography
With the advancement of opto-electronic technology, 3D patterns with sub micrometer dimensions are more and more integrated in the device, especially on imaging and AR/VR systems. To fabricate such 3D structures using standard lithography technique requires numerous process steps: multiple lithography and pattern transfer, which is time and resource consuming.
With optical grayscale lithography, such 3D structures can be fabricated in single lithography step, therefore reducing significantly the number of process steps required in standard lithography. For high volume manufacturing of such 3D patterns, optical grayscale lithography with Deep-UV (DUV), 248nm and 193nm are the most relevant, as it is compatible with industrial production line. This technique of 3D lithography is however more complex than it seems, which requires advance lithography model and data-preparation flow to design optical mask corresponding to the desired 3D pattern.
Modeling and experimental validation of a catalytic reactor and optimization of the process for the production of e-Biofuels
During the past 20 years, « Biomass-to-liquid » processes have considerably grown. They aim at producing a large range of fuels (gasoline, kerozene, diesel, marine diesel oil) by coupling a biomass gazéification into syngaz unit (CO+CO2+H2 mixture) and a Fischer-Tropsch (FT) synthesis unit. Many demonstration pilots have been operated within Europe. Nevertheless, the low H/C ratio of bio-based syngaz from gasification requires the recycling of a huge quantity of CO2 at the inlet of gaseification process, which implies complex separation and has a negative impact on the overall valorization of biobased carbon. Moreover, the possibility to realize, in the same reactor, the Reverse Water Gas Shift (RWGS) and Fischer-Tropsch (FT) reaction in the same reactor with promoted iron supported catalysts has been proved (Riedel et al. 1999) and validated in the frame of a CEA project (Panzone, 2019).
Therefore, this concept coupled with the production of hydrogen from renewable electricity opens new opportunities to better valorize the carbon content of biomass.
The PhD is based on the coupled RWGS+FT synthesis in the same catalytic reactor. On the one hand a kinetic model will be developed and implemented in a multi-scale reactor model together with hydrodynamic and thermal phenomena. The model will be validated against experimental data and innovative design will be proposed and simulated. On the other hand, the overall PBtL process will be optimized in order to assess the potential of such a process.
Experimental study of boundary layers in turbulent convection by diffusive waves spectroscopy
Turbulent convection is one of the main drivers of geophysical and astrophysical flows, and is therefore a key element in climate modeling. It is also involved in many industrial flows. Transport efficiency is often limited by boundary layers whose nature and transitions as a function of control parameters are poorly understood.
The aim of this thesis will be to set up a convection experiment to probe the dissipation rate in boundary layers in the turbulent regime, using an innovative technique developed in the team: multi-scattered wave spectroscopy.
Design and fabrication of neuromorphic circuit based on lithium-iontronics devices
Neural Networks (NNs) are inspired by the brain’s computational and communication processes to efficiently address tasks such as data analytics, real time adaptive signal processing, and biological system modelling. However, hardware limitations are currently the primary obstacle to widespread adoption. To address this, a new type of circuit architecture called "neuromorphic circuit" is emerging. These circuits mimic neuron behaviour by incorporating high parallelism, adaptable connectivity, and in memory computation. Ion gated transistors have been extensively studied for their potential to function as artificial neurons and synapses. Even if these emerging devices exhibit excellent properties due to their ultra low power consumption and analog switching capabilities, they still need to be validated into larger systems.
At the RF and Energy Components Laboratory of CEA-Leti, we are developing new lithium-gated transistors as building blocks for deploying low-power artificial neural networks. After an initial optimization phase focused on materials and design, we are ready to accelerate the pace of development. These devices now need to be integrated into a real system to assess their actual performance and potential. In particular, both bio-inspired circuits and crossbar architectures for accelerated computation will be targeted.
During this 3-year PhD thesis, your (main) objective will be to design, implement, and test neural networks based on lithium-gated transistor crossbars (5x5, 10x10, 20x20) and neuromorphic circuits , along with the CMOS read and write logic to control them. The networks might be implemented using different algorithms and architectures, including Artificial Neural Network, Spiking Neural Networks and Recurrent Neural Networks, which will be then tested by solving spatial and/or temporal pattern recognition problems and reproduce biological functions such as pavlovian conditioning.
Embedded local blockchain on secure physical devices
The blockchain is based on a consensus protocol, the aim of which is to share and replicate ordered data between peers in a distributed network. The protocol stack, embedded in the network's peer devices, relies on a proof mechanism that certifies the timestamp and ensures a degree of fairness within the network.
The consensus protocols used in the blockchains deployed today are not suitable for embedded systems, as they require too many communication and/or computing resources for the proof. A number of research projects, such as IOTA and HashGraph, deal with this subject and will be analysed in the state of the art.
The aim of this thesis is to build a consensus protocol that is frugal in terms of communications and computing resources, and whose protocol stack will be implemented in a secure embedded device. This protocol must be based on the proof of elapsed time from our laboratory's work, which is also frugal, called Proof-of-Hardware-Time (PoHT), and must satisfy the properties of finality and fairness. The complete architecture of a peer node in the network will be designed and embedded on an electronic board including a microprocessor and several hardware security components, in such a way that the proof resource cannot be parallelized. Communication between peers will be established in a distributed manner.
Advancing Semantic Representation, Alignment, and Reasoning in Multi-Agent 6G Communication Systems
Semantic communications is an emerging and transformative research area, where the focus shifts from transmitting raw data to conveying meaningful information. While initial models and design solutions have laid foundational principles, they often rest on strong assumptions regarding the extraction, representation, and interpretation of semantic content. The advent of 6G networks introduces new challenges, particularly with the growing need for multi-agent systems where multiple AI-driven agents interact seamlessly.
In this context, the challenge of semantic alignment becomes critical. Existing literature on multi-agent semantic communications frequently assumes that all agents share a common understanding and interpretation framework, a condition rarely met in practical scenarios. Misaligned representations can lead to communication inefficiencies, loss of critical information, and misinterpretations.
This PhD research aims to advance the state-of-the-art by investigating the principles of semantic representation, alignment, and reasoning in multi-AI agent environments within 6G communication networks. The study will explore how agents can dynamically align their semantic models, ensuring consistent interpretation of messages while accounting for differences in context, objectives, and prior knowledge. By leveraging techniques from artificial intelligence, such as machine learning, ontology alignment, and multi-agent reasoning, the goal is to propose novel frameworks that enhance communication efficiency and effectiveness in multi-agent settings. This work will contribute to more adaptive, intelligent, and context-aware communication systems that are key to the evolution of 6G networks.
Development of an advanced grade of nano-reinforced austenitic steel for use under intense flux
Recent work has shown that it is possible to obtain ODS (Oxide Dispersion Strengthened) austenitic steels for use under intense flux. These new grades are beginning to be studied for nuclear applications around the world. They should have remarkable properties, particularly in terms of resistance to swelling under irradiation and creep, thanks to the addition of nano-reinforcements in exceptional density (10^23 to 10^24 m-3). These ODS steels are obtained by powder metallurgy, by co-grinding a metal powder with an oxide powder. The aim of this work is to succeed in manufacturing, using an innovative process, ODS austenitic steel cladding tubes. It will be necessary to master the recrystallization of these tubes, to propose a first critical evaluation by specifying the property/microstructure relationships and by evaluating, by irradiation with charged particles, the behavior under irradaition of this new material. The student will be trained in Scanning Electron Microscopy and the techniques that result from it (X-ray analysis, EBSD, etc.), in SAXS, in the performance and exploitation of mechanical tests. He will have to acquire good notions in Transmission Electron Microscopy and in Tomographic Atomic Probe. The understanding of the behavior under irradiation will be guided by simulations by cluster dynamics.
Study and simulation of phase entrainment in mixer-settler batteries
As part of the development of new liquid-liquid extraction separation processes, experimental tests are implemented to demonstrate the recovery of valuable elements sufficiently decontaminated from impurities. These tests are commonly carried out in mixer-settler batteries. However, depending on the operating conditions, these finished products may be contaminated by impurities. This contamination results from the combination of several factors:
-Hydrodynamic: Entrainment in the solvent of non-decanted aqueous drops containing impurities
-Chemical: the impurity separation factor is low (less than 10-3)
-Process: the entrainment of drops is amplified with the increase in the rate (reduction of the residence time of the drops)
This thesis aims to increase the understanding of the different phenomena responsible for these phase entrainments in order to estimate optimal operating parameters and to guarantee a contamination of the finished products below a fixed threshold. The aim will be to develop a macroscopic model to predict the flow rate of non-decanted droplets as a function of the operating conditions in the mixer-settler batteries. It will have to be based on hydrodynamic simulations coupling the resolution of a droplet population balance to a continuous phase flow. A coupling will be carried out between this hydrodynamic model and the PAREX or PAREX+ code to size the process diagrams. The qualification of the proposed models will have to be done by comparisons with experimental measurements (based on previous or future test campaigns).
Combined Software and Hardware Approaches for Large Scale Sparse Matrix Acceleration
Computational physics, artificial intelligence and graph analytics are important compute problems which depend on processing sparse matrices of huge dimensions. This PhD thesis focuses on the challenges related to efficiently processing such sparse matrices, by applying a systematic software are hardware approach.
Although the processing of sparse matrices has been studied from a purely software perspective for decades, in recent years many dedicated, and very specific hardware, accelerators for sparse data have been proposed. What is missing is a vision of how to properly exploit these accelerators, as well as standard hardware such as GPUs, to efficiently solve a full problem. Prior to solving a matrix problem, it is common to perform pre-processing of the matrix. This can include techniques to improve the numerical stability, to adjust the form of the matrix, and techniques to divide it into smaller sub-matrices (tiling) which can be distributed to processing cores. In the past, this pre-processing has assumed homogenous compute cores. New approaches are needed, to take advantage of heterogeneous cores which can include dedicated accelerators and GPUs. For example, it may make sense to dispatch the sparsest regions to specialized accelerators and to use GPUs for the denser regions, although this has yet to be shown. The purpose of this PhD thesis is to take a broad overview of the processing of sparse matrices and to analyze what software techniques are required to exploit existing and future accelerators. The candidate will build on an existing multi-core platform based on RISC-V cores and an open-source GPU to develop a full framework and will study which strategies are able to best exploit the available hardware.
Enhancing Communication Security Through Faster-than-Nyquist Transceiver Design
In light of the growing demand for transmission capacity in communication networks, it is essential to explore innovative techniques that enhance spectral efficiency while maintaining the reliability and security of transmission links. This project proposes a comprehensive theoretical modeling of Faster-Than-Nyquist (FTN) systems, accompanied by simulations and numerical analyses to evaluate their performance in various communication scenarios. The study will aim to identify the necessary trade-offs to maximize transmission rates while considering the constraints related to implementation complexity and transmission security, a crucial issue in an increasingly vulnerable environment to cyber threats. This work will help identify opportunities for capacity enhancement while highlighting the technological challenges and adjustments necessary for the widespread adoption of these systems for critical and secure links.