Development of algorithms and modeling tools of Low-Energy Critical Dimension Small Angle X-ray Scattering
This PhD will take place at the CEA–LETI, a major European actor in the semiconductor industry, and more precisely, at the Nanocharacterization platform of the CEA–LETI witch offer world-class analytical techniques and state-of-the-art instruments. Our team aims to accompany the industry in the development of new characterization tools and so to meet the metrological needs of future technological nodes. Over the past few years, pioneer developments on a new metrology technique based on hard x-ray scattering called CD-SAXS were done at the PFNC. This technique is used to reconstruct the in-plane and out-of-plane structure of nanostructured thin-films with a sub-nm resolution. In this project, we are looking to extend the CD-SAXS approach leveraging the recent breakthrough in the development of low-energy x-ray sources (A. Lhuillier et al. 1988, Nobel prize 2023) called High Harmonics Generation (HHG) sources. Therefore, you will participate in the development of a new and promising characterization methods called Low-energy critical dimension small angle x-ray scattering. The very first proof of concept of this new measurement was conducted in November 2023.
Mission:
In order to include in the data reduction the measurement specificities of this new approach (multi-wavelength, low energy, …) your mission will focus on several aspects to explore in parallel:
- Develop new modeling tools to analyze the data:
o Finite element simulations with Maxwell solver
o Analytical Fourier Transform (similar to standard CD-SAXS) vs dynamical theory
o Comparison between the two approaches
- Build new models dedicated to lithography problematic (CD, overlay, roughness)
- Define the limitations of the technique through the simulation (in term of resolution (nm), uncertainty)
This work will support the development of CD-SAXS measurements with a laboratory HHG (High Harmonic Generation) source lead by a Postdoctoral fellow.
Accelerating thermo-mechanical simulations using Neural Networks --- Applications to additive manufacturing and metal forming
In multiple industries, such as metal forming and additive manufacturing, the discrepancy between the desired shape and the shape really obtained is significant, which hinders the development of these manufacturing techniques. This is largely due to the complexity of the thermal and mechanical processes involved, resulting in a high computational simulation time.
The aim of this PhD is to significantly reduce this gap by accelerating thermo-mechanical finite element simulations, particularly through the design of a tailored neural network architecture, leveraging theoretical physical knowledge.
To achieve this, the thesis will benefit from a favorable ecosystem at both the LMS of École Polytechnique and CEA List: internally developed PlastiNN architecture (patent pending), existing mechanical databases, FactoryIA supercomputer, DGX systems, and 3D printing machines. The first step will be to extent the databases already generated from finite element simulations to the thermo-mechanical framework, then adapt the internally developed PlastiNN architecture to these simulations, and finally implement them.
The ultimate goal of the PhD is to demonstrate the acceleration of finite element simulations on real cases: firstly, through the implementation of feedback during metal printing via temperature field measurement to reduce the gap between the desired and manufactured geometry, and secondly, through the development of a forging control tool that achieves the desired geometry from an initial geometry. Both applications will rely on an optimization procedure made feasible by the acceleration of thermo-mechanical simulations.
Physics of perovskite materials for medical radiography: experimental study of photoconduction gain
X-rays is the most widely used medical imaging modality. It is used to establish diagnostics, monitor the evolution of pathologies, and guide surgical procedures.
The objective of this thesis is to study a perovskite type semiconductor material for its use as a direct X-ray sensor. Perovskite-based matrix imagers demonstrate improved spatial resolution and increased signal, and can thus help improve patient treatment. Prototype X-ray imagers manufactured at the CEA already provide radiographic images but their performances are limited by the instability of the sensor material.
You job will be to study the mechanisms responsible for the photoconduction gain and photocurrent drift of thick perovskite layers from both a theoretical and an experimental standpoint. To this end, you will adapt the electro-optical characterization benches of the laboratory, conduct experiments and analyze the data collected. You will also have the opportunity to perform advanced characterizations with specialized laboratories within the framework of national and international collaborations. The results of this thesis will provide a better understanding of the material properties and guide its ellaboration to produce high-performance X-ray imagers.
Thermomechanical study of heterostructures according to bonding conditions
For many industrial applications, the assembly of several structures is one of the key stages in the manufacturing process. However, these steps are generally difficult to carry out, as they lead to significant increases in warpage. Controlling stresses and strains generated by heterostructures is however imperative. We proposes to address this subject using both experimental exploration and simulation through thermomechanical studies in order to predict and anticipate problems due to high deformations.
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.
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.
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.