Cryogenic characterization of emerging memories for space and/or quantum computing applications
Low temperature computing is a new proposal to boost the technological performances beyond the frontiers in the aerospace, high performance servers, quantum computing and data center domain.
Different emerging technologies have been showing promising features at single device level during the ongoing Ph.D. work: the programmability of the OxRAMs was proved down to 4K and efforts were focused on the understanding of the interactions between the selector and the resistor composing the memory cell. FeRAMs show a better programming efficiency and stability at low temperature probably due to a crystallographic change driven by the program operation; hypothesis that lies unproved. PCM also showed a programmability down to 12K and may be included in the analysis.
Statistical behavior of R&D chip at low temperature will be the key theme of this proposition knowing that very few publications appeared in the scientific literature leaving much room for analysis and comprehension.
Throughout this Ph. D., you will gain a broad spectrum of knowledge, spanning cryogenics, microelectronics reliability and device physics. Different technologies developed in LETI will be statistically screened in this innovative scenario. Modeling of the conduction phenomena might be also considered. You will be part of a team of 7-8 people between permanent, researchers and students and you will be managed to share your work with them.
Study of the stability of Si-CMOS Structures for the implementation of Spin Qubits
Silicon-based spin qubits in CMOS structures stand out for their compatibility with semiconductor technologies and their scalability potential. However, impurities and defects introduced during fabrication lead to noise and instability, which affect their performance.
The objective is to characterize devices fabricated at CEA-Leti, from room temperature to cryogenic temperatures, to evaluate their quality and understand the physical mechanisms responsible for their instability. The goal is to improve the design of the devices and ideally establish a method to identify the most promising devices without requiring measurements at very low temperatures.
The candidate should have skills in the following areas:
- Experimental physics and semiconductors.
- Algorithm programming and data analysis.
- Knowledge in nanofabrication, low-temperature physics, and quantum physics (desirable).
Analysis of solid oxide cell degradation by transmission electron microscopy and atomic probe tomography
Nowadays, high-temperature electrolysis is considered as one of the most promising technology for producing green hydrogen. The electrolysis reaction takes place in a Solid Oxide Cell (SOC) composed of an oxygen electrode (made of LSCF or PrOx) and a hydrogen electrode (made of Ni-YSZ) separated by an electrolyte (made of YSZ). To accompany industrialization f SOCs, the durability still needs to be improved. The main performance losses are due to the degradation of the two electrodes. In order to propose an improvement, it is essential to gain a precise understanding of electrode degradation mechanisms. In this thesis, we thus propose to apply high-resolution transmission electron microscopy and atom probe tomography (SAT) to study electrode degradation after aging under current. On the one hand, advanced electron microscopy techniques coupled with energy dispersive X-ray spectroscopy (EDX) and electron energy loss spectroscopy (EELS) will be applied. In addition, analyses carried out on a SAT will provide three-dimensional information particularly suited to the complex structure of the electrodes.
This work should provide a better understanding of the degradation mechanisms of high-temperature electrolysis cells. Recommendations for their manufacture can then be made to improve their lifespan.
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.
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.
Advanced Surface Analysis of Ferroelectrics for memory applications
CEA-Leti has a robust track record in memory technology. This PhD project aims to contribute to the development of HfO2-based ferroelectric devices. One of the major challenges in this field is to stabilize the orthorhombic phase while reducing film thickness and thermal budget. To gain a deeper understanding of the underlying mechanisms, a novel sample preparation method will be adapted from a previous PhD project and further developed for application to ferroelectric memories. This method involves creating a beveled crater that exposes the entire thickness of the film, allowing for access by multiple characterization techniques (XPS, TOF-SIMS, SPM) on the same area. This approach will enable the correlation of compositional and chemical measurements with electrical properties. Furthermore, heating and biasing within advanced surface characterization instruments (TOF-SIMS, XPS) will provide insights into how device performance is influenced by compositional and chemical changes.
You possess strong experimental skills and a keen interest in state-of-the-art surface analysis instruments. You excel in team environments and will have the opportunity to collaborate with experts across a wide range of techniques on the nanocharacterization platform, including advanced numerical data treatment. Proficiency in Python or similar programming languages is highly desirable.
3D chemical analysis of downscaled ePCM devices for sub-18 nm technology nodes using STEM-EDX tomography and machine learning tools
The context of this PhD is the recent progress of Phase-Change Memory technology in the embedded applications (ePCM). The ultimate scaling of ePCM for sub-18nm nodes poses many challenges not only in fabrication, but also in the physico-chemical characterization of these devices. The aim of the project is to study the 3D chemical segregation/crystallization phenomena in new PCM alloys integrated into planar and vertical ePCM scaled devices, using electron tomography in STEM-EDX (and 4D-STEM) mode. Given the extreme downscaling and the complex geometry of the devices, the focus will be on optimizing experimental conditions and applying machine learning and deep learning techniques to improve the quality and reliability of the obtained 3D results. A correlation with the device electrical behavior will be carried out to better understand the phenomena behind failures after endurance and after data loss at high temperatures.
A probe-corrected Cold-FEG NeoARM TEM (60kV-200kV) will be used for the tomographic data acquisition. It is equipped with two large solid angle SSD detectors (JEOL Centurio), a CEOS Energy-Filtering and Imaging Device (CEFID) and a Timepix3 direct electron camera. The candidate will also have access to in-house Python codes as well as to the computing resources needed to carry out the spectral and tomographic data analysis.
X-ray diffusion assisted by Artificial Intelligence: the problem of the representativeness of synthetic databases and the indistinguishability of predictions.
The advent of artificial intelligence makes it possible to accelerate and democratize the processing of small-angle X-ray scattering (SAXS) data, an expert technique for characterizing nanomaterials that allows to determine the specific surface area, volume fraction and characteristic sizes of structures between 0.5 to 200 nm.
However, there is a double problem around SAXS assisted by Artificial Intelligence: 1) the scarcity of data requires training the models on synthetic data, which poses the problem of their representativeness of real data, and 2) the laws of physics stipulate that several candidate nanostructures can correspond to a SAXS measurement, which poses the problem of the indistinguishability of predictions. This thesis therefore aims to build an artificial intelligence model adapted to SAXS trained on experimentally validated synthetic data, and on the expert response which weights the categorization of predictions by their indistinguishability.