Study of new photodiode architecture for IR imagers
In the field of high-performance infrared detection, CEA-LETI plays a leading role in the development of the HgCdTe material, which today offers such performance that it is integrated into the James Webb Space Telescope (JWST) and allows the observation and study of deep space with unparalleled precision to date. However, we believe that it is still possible to make a significant step forward in terms of detection performance. Indeed, it seems that a fully depleted structure, called a PiN photodiode, could further reduce the dark current (and thus reduce noise and gain sensitivity at low photonic flux) compared to the non-fully depleted structures currently used. This architecture would represent the ultimate photodiode and would allow either a further increase in performance at a given operating temperature or a significant increase in the operating temperature of the detector, with the potential to open new fields of application by greatly simplifying cryogenics.
Your role in this thesis work will be to contribute to the development of the ultimate photodiode for very high-performance infrared detection, characterize and simulate the PiN photodiodes in HgCdTe technology manufactured on our photonic platform.
Candidate Profile:
You hold a Master's degree in optoelectronics and/or semiconductor material physics and are passionate about applied research.
The main technical skills required are: semiconductor component physics, optoelectronics, data processing, numerical simulations, interest in experimental work to carry out characterizations in a cryogenic environment but also theoretical work to carry out numerical simulations.
The PhD student will be integrated into a multidisciplinary team ranging from the growth of II-VI materials to electro-optical characterization, including microelectronics manufacturing processes in clean rooms and the packaging issues of such objects operating at low temperature.
High-Endurance Chalcogenide Memories for Next-Generation AI
Discover a unique phd opportunity where you will dive into the heart of innovation in memory technologies. You will develop strong expertise in areas such as electrical characterization and the understanding of degradation phenomena in chalcogenide-based memories.
By joining our multidisciplinary teams, you will play a key role in studying and improving the endurance of Phase-Change Memory (PCM) and Threshold Change Memory (TCM) devices—two promising technologies for high-performance artificial intelligence applications. You will take part in innovative projects combining scientific rigor and applied research on nanoscale devices, working closely with another CEA PhD student who conducts advanced physico-chemical analyses (TEM) to investigate degradation mechanisms.
You will have the opportunity to contribute actively to tasks such as:
Electrical characterization of PCM and TCM devices to analyze cycling-induced degradation
Development and evaluation of innovative programming protocols to extend endurance limits
Proposing solutions to improve the reliability and performance of next-generation memories
Regular collaboration and discussion with the CEA PhD student to interpret TEM results and draw conclusions about degradation mechanisms
Dies to wafer direct bonding: from physical mechanisms to the development of thin stackable dies
Direct dies-to-wafer bonding has become, in recent years, a major development axis in microelectronics and at the heart of many LETI projects, both in silicon photonics and for 3D applications involving hybrid bonding.
Due to their small size, die bonding allows the study of direct bonding edge effects and the implementation of new direct bonding processes that can shed original light on the mechanisms of direct bonding, which are already well studied at LETI. From a more technological perspective, the development of thin stackable chips will also be a very interesting technological key for many applications. This approach is a clever alternative to classical damascene processes to address the challenges related to the planarization of surfaces with low density of high topographies.
Selective deposition of oxides by ALD
For next-generation microelectronics, Area Selective Deposition (ASD)is a promising approach to simplify integration schemes for the most advanced technology nodes. These ASD approaches need to be adapted according to a trio comprising the material to be deposited, the growth surface, and the inhibited surface.
This PhD focuses on the area selective deposition of oxides (such as SiO2, Al2O3, …) on Si or SiO2 and not on silicon nitride (SiN), which is one of the most complex topics in ASD, and aims to evaluate the relevance of this type of process for simplifying the integration and the fabrication of advanced FDSOI transistors.
To develop this selective oxide deposition process, various approaches aiming at making SiN an inhibitor of the Atomic Layer Deposition (ALD) will be explored (plasma treatments, Small Molecular Inhibitors, combination of both, etc.). Dedicated surface characterizations will be carried out in order to better understand the mechanisms of inhibition at the origin of the selective deposition and allowing to achieve high selectivity for oxide thicknesses of 10 nm and above.
This PhD project will take place at CEA-LETI, within the advanced materials deposition department, in collaboration with LMI UMR 5615 CNRS/UCBLyon. The student will have access to the CEA-LETI 300 mm cleanroom fabrication platforms for thin film deposition by PEALD, the CEA nanocharacterization platform and gas-phase surface functionalization at LMI. Surface analyses and thin film characterizations (ellipsometry, XRR, AFM, FTIR, contact angle, SEM, XPS, ToF-SIMS) will be used to determine the best selectivity and understand the physico-chemical mechanisms.
Integration of security functions for imagers: encryption, watermarking using compact functions close to the sensor
Illicit uses of images dramatically rise with deepfake content manipulation or unauthorized access. Securing images from their source i.e., at the image sensor level, is key to address the challenges of this field of cybersecurity. The "trusted imagers" addresses the need to ensure image security, authentication, and encryption starting at the point
of acquisition.
Building on our initial research, your PhD thesis will focus on finding innovative solutions to integrate security functions into image sensors with the challenge of meeting the requirements of low power consumption and compact integrated architecture, while keeping a high level of security. After an initial phase aiming at the development of the skills specific to the thesis, and depending on your background and interests, your work will involve:
?
Developing encryption and/or watermarking algorithms in Python to evaluate their
complexity, then proposing compact versions compatible with integration into image
sensors.
?
Evaluating the impact of algorithmic choices and hardware implementation on image
quality.
?
Designing and validating hardware architectures that implement the algorithms.
?
Designing the integrated circuits implementing these functions.
With the ultimate goal of fabricating an integrated circuit, the work will be conducted at CEA
Leti ,
using professional IC design tools and software development environments.
Development of vertical GaN power transistors gate module
This PhD topic offers a unique opportunity to enhance your skills in GaN power devices and develop cutting-edge architectures. You’ll work alongside a multidisciplinary team specializing in material engineering, characterization, device simulation, and electrical measurements. If you’re eager to innovate, expand your knowledge, and tackle state-of-the-art challenges, this position is a valuable asset to your career!
Vertical GaN power components are highly promising for applications beyond the kV range and are therefore extensively studied worldwide. Transistors with a 'trench MOSFET' architecture have been demonstrated in the state-of-the-art with very encouraging results. The gate stack of these devices is a crucial element as it directly impacts their on-state resistance, threshold voltage, and the control signal to be applied in a power converter. The proposed study will focus on developing innovative gate stacks that can withstand high gate voltages while maintaining state-of-the-art threshold voltage and channel mobility with minimal gate dielectric trapping. The work will involve studying the impact of process parameters on electrical characteristics. Special attention will be given to optimizing the gate geometry through TCAD simulations to study how its shape impacts on-state and breakdown. Identified improvements will be integrated to the devices fabricated on our 200mm GaN power devices line. The work will take place within the power devices lab and will be supported by several ongoing projects.
Ultra-low frequency wireless power transmission for sensor node charging
Wireless power transfer (WPT) technologies are rapidly expanding, particularly for wireless charging of everyday electronic devices and for powering wireless communicating sensor nodes. However, their transmission ranges remain limited, and the high operating frequencies typically used prevent energy transfer in the presence of, or through, conductive media (such as metallic barriers or seawater). This constraint significantly limits their adoption in complex environments (industrial, biomedical, etc.).The ultra-low-frequency technology investigated in our laboratory is based on an electromechanical receiver system comprising a coil and a magnet set into motion by a remotely generated magnetic field. The objective of this PhD project is to propose and develop novel ultra-low-frequency concepts to increase transmission range while maintaining sufficient power density for supplying sensor systems. The work will therefore involve studying, designing, optimizing, and experimentally validating the performance of new topologies (emitter field shaping, receiver geometries and materials, etc.). The candidate will develop analytical and numerical models to identify key system parameters and compare performance with the state of the art (range, power density, sensitivity to orientation). In addition, the candidate will propose, design, and experimentally evaluate innovative energy conversion electronics, on the transmitter and/or receiver side, to assess their impact on the overall system performance. A joint optimization of the electromechanical system and its associated power electronics will ultimately lead to the realization of a high-performance wireless power transfer system. A multidisciplinary profile with a strong orientation toward physics and mechatronics is sought for this PhD project. In addition to solid theoretical foundations, the PhD candidate must demonstrate the ability to work effectively in a team environment as well as a strong aptitude for experimental work. The PhD candidate will be integrated into the Systems Department of CEA-Leti, within a team of researchers with strong expertise in the development and optimization of electronic and mechatronic systems, combining innovative solutions for energy harvesting, wireless power transfer, low-power electronics, and sensor integration aimed at the development of autonomous systems.
Development of an integrated solid state nanopore analysis system
The identification of biological material (DNA, RNA, proteins,…) is generally done thanks to cumbersome lab equipment and/or rely on ultra-specific and proprietary sensitive reagents. We aim to develop a new platform based on the solid-state nanopore technology which could produce label-free results on field.
One way to pierce a nanopore in an ultra-fine dielectric membrane is to use an electron beam. An ion current is obtained when placing this pierced membrane in-between two insulated reservoirs filled with electrolytes and applying a low voltage. A particle going through the pore modifies this ionic current giving us information on its size, charge or conformation.
For this technique to yield the best results we need control over each bit of the platform: the dielectric assembly and nanopore within; the high speed and precision electronic apparatus to measure ionic current; the fluidic integration and even the algorithm responsible for deciphering the current trace. Starting from the simplest setup possible, the PhD candidate will have to push forward every aspect of this ambitious project, aiming for protein sequencing, relying on the multiple expertise of the Leti and the Lambe laboratory.
Lightweight CNN and Causal GNN for scene understanding
Scene understanding is a major challenge in computer vision, with recent approaches dominated by transformers (ViT, LLM, MLLM), which offer high performance but at a significant computational cost. This thesis proposes an innovative alternative combining lightweight convolutional neural networks (Lightweight CNN) and causal graph neural networks (Causal GNN) for efficient spatio-temporal analysis while optimizing computational resources. Lightweight CNNs enable high-performance extraction of visual features, while causal GNNs model dynamic relationships between objects in a scene graph, addressing challenges in object detection and relationship prediction in complex environments. Unlike current transformer-based models, this approach aims to reduce computational complexity while maintaining competitive accuracy, with potential applications in embedded vision and real-time systems.
Development of automatic gamma spectrum analysis using a hybrid machine learning algorithm for the radiological characterization of nuclear facilities decommissioning.
The application of gamma spectrometry to radiological characterization in nuclear facility decommissioning, requires the development of specific algorithms for automatic gamma spectrum analysis. In particular, the classification of concrete waste according to its level of contamination, is a crucial issue for controlling decommissioning costs.
Within CEA/List, LNHB, in collaboration with CEA/DEDIP, has been involved for several years in the development of tools for the automatic analysis of low-statistics gamma spectra, which can be applied to scintillator detectors (NaI(Tl), plastics). In this context, an original approach based on a hybrid machine learning/statistics spectral unmixing algorithm has been developed for the identification and quantification of radionuclides in the presence of significant deformations in the measured spectrum, due in particular to interactions between the gamma emission from the radioactive source and its environment.
The proposed subject follows on from thesis work that led to the development of the hybrid algorithm with the aim of extending this approach to the radiological characterization of concrete surfaces. The candidate will be involved in the evolution of the hybrid machine learning/statistical algorithm for the characterization of concrete for classification as conventional waste. The work will include a feasibility study of modeling the deviations of the learned model to optimize the robustness of decision-making.