Automatization of quantum computing kernel writing for quantum applications
The framework of Hamiltonian simulation opens up a new range of computational approaches for quantum computing. These approaches can be developed across all relevant fields of quantum computing applications, including, among others, partial differential equations (electromagnetism, fluid mechanics, etc.), quantum machine learning, finance, and various methods for solving optimization problems (both heuristic and exact).
The goal of this thesis is to identify a framework where these approaches—based on Hamiltonian simulation or block-encoding techniques—are feasible and can be written in an automated way.
This work could extend to the prototyping of a code generator, which would be tested on practical cases in collaboration with European partners (including a few months of internship within their teams).
From Combustion to Astrophysics: Exascale Simulations of Fluid/Particle Flows
This thesis focuses on the development of advanced numerical methods to simulate fluid-particle interactions in complex environments. These methods, initially used in industrial applications such as combustion and multiphase flows, will be enhanced for integration into simulation codes for exascale supercomputers and adapted to meet the needs of astrophysics. The objective is to enable the study of astrophysical phenomena such as the dynamics of dust in protoplanetary disks and the structuring of dust in protostars and the interstellar medium. The expected outcomes include a better understanding of planetary formation mechanisms and disk structuring, as well as advancements in numerical methods that will benefit both industrial and astrophysical sciences.
Increasing the electrothermal robustness of new SiC devices
Silicon Carbide (SiC) is a semiconductor with superior intrinsic properties than Silicon for high temperature and high power electronics applications. SiC devices are expected to be extensively used in the electrification transition and novel energy management applications. To fully exploit the SiC superior properties, the future semiconductor devices will be used under extreme biasing and temperature conditions. These devices must operate safely at higher current densities, higher dV/dt and higher junction temperatures than Si devices does.
The objective of this thesis is to study the SiC devices fabricated at LETI under these extreme operating conditions, and to optimize their design to fully use the theoretical potential of SiC. The thesis work will include several phases that will be strongly coupled:
- Advanced electro-thermal characterisation (50%), by proposing new approaches to testing components in a box or on a suitable support, using artificial intelligence (AI) tools for data extraction and processing. The work will include adapting standard measurement methodologies to the specific switching characteristics of SiC.
- An assessment (15%) of the design and technological parameters responsible for the operating limits of the components.
- A physico-chemical characterisation component (15%) to analyse failures under these extreme conditions.
- The inclusion of predictive models (20%) for the sensitivity of architectures to extreme conditions and faults, based on modelling.
Design and optimization of color routers for image sensors
Color routers represent a promising technology that could revolutionize the field of image sensors. Composed of nanometricstructures called metasurfaces, these devices allow the modification of light propagation to improve the quantum efficiency of pixels. Thanks to recent technical advances, it is now possible to design and manufacture these structures, paving the way for more efficient image sensors.
The thesis topic focuses on the design and optimization of color routers for image sensors. Several research avenues will be explored, such as the implementation of new metasurfacegeometries (`freeform`) or innovative configurations to reduce pixel pitch (0.5µm or 0.6µm). Various optimization methods can be used, such as the adjointmethod, machine learning, or the use of auto-differentiable solvers. The designs must be resilient to the angle of light incidence and expected variations during manufacturing. After this simulation phase, the proposed structures will be manufactured, and the student will have the mission to characterize the chips and analyze the obtained results (quantum efficiency, modulation transfer function...).
This thesis will be co-supervised by STMicroelectronics and CEA LETI in Grenoble. The student will be integrated into the teams of engineer-researchers working on this project. He/she will be led to collaborate with various specialists in various fields such as lithography and optical characterization.
The student's main activities:
- Optical simulation using numerical methods (FDTD, RCWA)
- Development of optimization methodologies for metasurfacedesign (adjointmethod, topological optimization...)
- Electro-optical characterization and analysis of experimental data
Predictive Diagnosis and Ageing Trajectory Estimation of New Generation Batteries through Multi-modalities Fusion and Physics-Informed Machine Learning
Context:
Lithium-ion and emerging Sodium-ion batteries are crucial for energy transition and transportation electrification. Ensuring battery longevity, performance, and safety requires understanding degradation mechanisms at multiple scales.
Research Objective:
Develop innovative battery diagnostic and prognostic methodologies by leveraging multi-sensor data fusion (acoustic sensors, strain gauge sensors, thermal sensors, electrical sensors, optical sensors) and Physics-Informed Machine Learning (PIML) approaches, combining physical battery models with deep learning algorithms.
Scientific Approach:
Establish correlations between multi-physical measurements and battery degradation mechanisms
Explore hybrid PIML approaches for multi-physical data fusion
Develop learning architectures integrating physical constraints while processing heterogeneous data
Extend methodologies to emerging Na-Ion battery technologies
Methodology:
The research will utilize an extensive multi-instrumented cell database, analyzing measurement signatures and developing innovative PIML algorithms that optimize multi-sensor data fusion and validate performance using real-world data.
Expected Outcomes:
The thesis aims to provide valuable recommendations for battery system instrumentation, develop advanced diagnostic algorithms, and contribute significantly to improving the reliability and sustainability of electrochemical storage systems, with potential academic and industrial impacts.
Sub-THz programmable electromagnetic surfaces based on phase change material switches
Spatiotemporal manipulation of the near- and far-electromagnetic (EM)-field distribution and its interaction with matter in the THz spectrum (0.1-0.6 THz) is of prime importance in the development of future communication, spectroscopy, imaging, holography, and sensing systems. Reconfigurable Intelligent (Meta)Surface (RIS) is a cutting-edge hybrid analogue/digital architecture capable of shaping and controlling the THz waves at the subwavelength scale. To democratize the RIS technology, it will be crucial to reduce its energy consumption by two orders of magnitude. However, the state-of-the-art does not address the integration, scalability, wideband and high-efficiency requirements.
Based on our recent research results, the main objective of this project will be to demonstrate novel silicon-based RIS architectures s at 140 GHz and 300 GHz. The enhancement of the THz RIS performance will derive from a careful choice of the silicon technology and, from novel wideband meta-atom designs (also called unit cell or element) with integrated switches based on PCM (phase change material). The possibility of dynamically controlling the amplitude of the transmission coefficients of the meta-atoms, besides their phase, will be also investigated. Near-field illumination will be introduced to obtain an ultra-low profile. To the best of our knowledge, this constitutes a new approach for the design of high-gain antennas in the sub-THz range.
Defense of scene analysis models against adversarial attacks
In many applications, scene analysis modules such as object detection and recognition, or pose recognition, are required. Deep neural networks are nowadays among the most efficient models to perform a large number of vision tasks, sometimes simultaneously in case of multitask learning. However, it has been shown that they are vulnerable to adversarial attacks: Indeed, it is possible to add to the input data some perturbations imperceptible by the human eye which undermine the results during the inference made by the neural network. However, a guarantee of reliable results is essential for applications such as autonomous vehicles or person search for video surveillance, where security is critical. Different types of adversarial attacks and defenses have been proposed, most often for the classification problem (of images, in particular). Some works have addressed the attack of embedding optimized by metric learning, especially used for open-set tasks such as object re-identification, facial recognition or image retrieval by content. The types of attacks have multiplied: some universal, other optimized on a particular instance. The proposed defenses must deal with new threats without sacrificing too much of the initial performance of the model. Protecting input data from adversarial attacks is essential for decision systems where security vulnerabilities are critical. One way to protect this data is to develop defenses against these attacks. Therefore, the objective will be to study and propose different attacks and defenses applicable to scene analysis modules, especially those for object detection and object instance search in images.
RF Circuit Design for Zero Energy Communication
Our ambition for 6G communication is to drastically reduce the Energy in IoT. For that purpose we aim at developing an integrated circuit enabling zero Energy communication.
The objective of this PhD is to design this circuit in FD-SOI and operating in the 2.4 GHz. In this PhD, we propose to use a new design technique which is currently revolutionizing the radio-frequency design. We expect that many innovations can be carried out during this PhD by combining those two innovations.
The candidate will integrate a large design team and he will participate in collaborative project at european level. As a first step, he will analyze the system constraints to choose the best architecture and derive the specifications. Then, he will formalize mathematically the performances of the backscattering technique in order to setup a design methodology. Then he will be working full time on circuit design, sending to fabrication two circuits in 22 um technology. He will be also involve in the test of the circuit as well as in the preparation of a demonstrator of the backscattering techniques. We expect to publish several papers in high level conferences.
Study of 3D pattern etch mechanisms into inorganic layers for optoelectronic applications
Optoelectronic devices such as CMOS Image Sensors (CIS) require the realization of 3D structures, convex microlenses, in order to focus photons towards the photodiodes defining the pixels. These optical elements are mandatory for the device efficiency. Their shape and dimension are critical for device performances. In the same way, devices based on diffractive optic and hyperspectral sensors are looking for complex multi-height structures. Finally, recent micro-display technologies for augmented reality (AR) and virtual reality (VR) require 3D structures difficult to achieve with conventional micro-fabrication technics.
Leti is at the state of the art on an alternative photolithography technics, so-called Grayscale. This process can produce a whole range of 3D structures not available with standard photolithography, such as concave, elliptic, pyramids and asymmetrical shapes. These structures could be used in a large number of application fields, like photonics and micro-displays (AR/VR). Once these structures achieved in photoresist, it is necessary to transfer them in an adapted functional layer using plasma etching. The etch mechanisms behind the transfer of micrometric 3D patterns into a polymer layer have been recently studied at Leti. To address new application needs, it is interesting to transfer these structures into silicon based inorganic layers because of their optical properties. Furthermore, the 3D pattern dimensions, currently few micrometers, need to be sub-micrometric for the most advanced technologies. In these condition, pattern transfer fidelity of 3D structures is even more challenging and it underlines why the etch mechanisms need to be well understood.
Currently the transfer into inorganic layers by plasma etching of submicronic 3D patterns obtained with Grayscale photolithography is not well studied in literature. Consequently, this thematic is innovative and has a real benefit. The goal of this PhD thesis is to study and understand the etch mechanisms in order to control the shape and dimension of the transferred structures. The work will be very experimental and will be mainly performed in Leti’s 300mm cleanroom. You will have access to a last generation plasma etch tool and numerous characterization technics. This thesis is in collaboration with the photolithography department and in interaction with different teams, such as the silicon platform and application department.
Learning world models for advanced autonomous agent
World models are internal representations of the external environment that an agent can use to interact with the real world. They are essential for understanding the physics that govern real-world dynamics, making predictions, and planning long-horizon actions. World models can be used to simulate real-world interactions and enhance the interpretability and explainability of an agent's behavior within this environment, making them key components for advanced autonomous agent models.
Nevertheless, building an accurate world model remains challenging. The goal of this PhD is to develop methodology to learn world models and study their use in the context of autonomous driving, particularly for motion forecasting and developing autonomous agents for navigation.