Etch and integration of phase change materials for reconfigurable photonic

Chalcogenide glasses are materials of interest for many applications: in phase-change memories, for example optical storage (CD-RW, DVD-RAM, Blu-ray Disks) or more recently Storage Class Memory, as a selector in 3D architecture resistive memories (OTS selector) or as an active medium for non-linear optics and reconfigurable photonics. In the latter case, the production of metasurfaces with controllable optical refractive indices and the development of photonic actuators is a subject of renewed interest, given the unique optical properties of these materials.
In this field, CEA-LETI is one of the major international players in the development of thin films of chalcogenide glasses and phase-change materials, as well as in the characterization and understanding of their unusual physical properties. Today, although these materials are well mastered at industrial level for memory applications, it is nevertheless necessary to work on integrating these innovative, high-performance materials into small-scale structures while preserving their unique physical properties. Initial integration studies carried out during a previous thesis showed that the etching and stripping process steps were far from being optimized, even though they are critical if the properties of the material are not to be degraded during the production of photonic structures. Although essential for process control, the etching mechanisms of chalcogenide glasses are relatively poorly described in the literature, with the exception of Ge2Sb2Te5 used in phase-change memories.
This theme is therefore innovative and has real added value. The aim of the thesis is therefore to study and understand the etching mechanisms of GeSbSeTe-based chalcogenide glasses in order to control the profile and size of the transferred structures. The work to be carried out is highly experimental and will take place mainly in the LETI's 300 mm clean room. The candidate will have access to state-of-the-art chalcogenide materials thin films, an industrially designed plasma etching reactor and a wide range of characterization facilities. This thesis will be carried out in collaboration with the LETI advanced materials deposition service and the optoelectronic devices applications department (DOPT).

Microstructural characterization by bulk laser-ultrasounds tomography

The proposed thesis falls into the framework of designing innovative methods in the materials characterization. The thesis aims to develop a new tomographic technique for characterizing microstructures using bulk laser ultrasound. In the state of the art, acoustic methods such as the scanning acoustic microscope and surface wave optoacoustic spectroscopy lead to grain imaging but only at the surface of the sample. However, industrial manufacturing processes (in metallurgy, welding, additive manufacturing...) can reveal spatial inhomogeneity of the microstructure, such as grain size gradients with depth within the component. Electron backscatter diffraction (EBSD) also provides surface imaging of grains but has disadvantages, including restriction on sample size and the need to make transversal cuts through the sample to image its volume.
The proposed idea is to develop a bulk laser ultrasound tomography technique able to determine the grain size in areas of a component or even to obtain imaging of large-grain microstructures and a local determination of crystallographic orientation. Therefore, the thesis's main objective will be to design such an experimental characterization tool and optimize its design using a digital twin to develop.

Conditional generative model for dose calculation in radiotherapy

Particle propagation through matter by Monte Carlo method (MC) is known for its accuracy but is sometimes limited in its applications due to its cost in computing resources and time. This limitation is all the more important for dose calculation in radiotherapy since a specific configuration for each patient must be simulated, which hinders its use in clinical routine.

The objective of this thesis is to allow an accelerated and thrifty dose calculation by training a conditional generative model to replace a set of phase space files (PSF), whose architecture will be determined according to the specificities of the problem (GAN, VAE, diffusion models, normalizing flows, etc.). In addition to the acceleration, the technique would produce an important gain in efficiency by reducing the number of particles to be simulated, both in the learning phase and in the generation of particles for the dose calculation (model's frugality).

We propose the following method:
- First, for the fixed parts of the linear accelerator, the use of a conditional generative model would replace the storage of the simulated particles in a PSF, whose data volume is particularly large. The compactness of the model would limit the exchanges between the computing units without the need for a specific storage infrastructure.
- In a second step, this approach will be extended to the final collimation whose complexity, due to the multiplicity of possible geometrical configurations, can be overcome using the model of the first step. A second conditional generative model will be trained to estimate the particle distribution for any configuration from a reduced number of simulated particles.

The last part of the thesis will consist in taking advantage of the gain in computational efficiency to tackle the inverse problem, i.e. optimising the treatment plan for a given patient from a contoured CT image of the patient and a dose prescription.

Natural language interactions for anomaly detection in mono and multi-variate time series using fondation models and retrieval augmented generation

Anomaly detection in mono and multi-variate time series highly depends on the context of the task. State-of-the-art approaches rely usually on two main approaches: first extensive data acquisition is sought to train artificial intelligence models such as auto-encoders, able to learn useful latent reprensations able to isolate abnormality from expected system behaviors; a second approach consists in careful features construction based on a combination of expert knowledge and artificial intelligence expert to isolate anomalies from normal behaviors using limited examples. An extensive analysis of the literature shows that anomaly detection refer to an ambiguous definition, because a given pattern in time series could appear as normal or abnormal depending on the application domain and the immediate context within the successive observed data points. Fondation models and retrieval-augmented generation has the potential to substantially modify anomaly detection approaches. The rationale is that domain expert, through natural language interactions, could be able to specify system behavior normality and/or abnormality, and a joint indexing of state-of-the-art literature and time series embedding could guide this domain expert to define a carefully crafted algorithm.

Contribution of metal-semiconductor interfaces to the operation of the latest generation of infrared photodiodes

This thesis concerns the field of cooled infrared detectors used for astrophysical applications. In this field, the DPFT/SMTP (Infrared Laboratory) of CEA-LETI-MINATEC works closely with Lynred, a world leader in the production of high-performance infrared focal planes. In this context, the infrared laboratory is developing new generations of infrared detectors to meet the needs of future products.
One of the current development axes concerns the quality of the p-type semiconductor metal interface. These developments are driven by the increase in the operating temperature of the detectors, as well as by the very strong performance requirements for space applications.
The challenge of this thesis is to contribute to a better understanding of the chemical species present at the interface of interest as a function of different surface treatment types and to link them to the electrical properties of the contact made.
The candidate will join the infrared laboratory, which includes the entire detector production process. He/she will produce these samples using the technological means available in the LETI clean room, in collaboration with experts in the field. He/she will also have access to the necessary characterization tools (SIMS, XPS, AFM…) available on the nano-characterization platform (PFNC) or in the CEA clean room. Finally, he/she will be involved in the electro-optical characterization of the material, in collaboration with the Cooled Infrared Imaging Laboratory (LIR), which specializes in fine material characterization.

Development of deposit/etch processes for SADP integration to FD10 node

Developement of new technologic nodes involves both a pattern dimension shrink and a pattern density increase. For the last years, development of multi-Patterning strategies with in particular Spacer Patterning (also called SADP) has signifcantly increased. This approach is based on a sacrificial pattern on which a material is depsosited with a conformal configuration to be etched and thus to define spacers used as masks to pattern the sublayer after sacrificial pattern removals. One of the main challenges of this intergration is the material choice in terms of compatibility (thermal budget, selectivity, etc.). Using SiCo(N)-based materials could be a favourable alternative to standard dielectrics (SiO2, SiN). Another challenge is to achieve only one population after SADP : to prevent microloading effects, etching process with an atomic controlwill be developped (ex. pulsing, ALE, etc.). Environmental footprint should be considered during these process developments.
The purpose of this thesis is to set an integration flow with SiCO(N)-based materials, to developp these materials and to determine associated etching strategies.
To lead your research activity, you would be able to benefit privileged environment offered by CEA-Leti with state of the art tool for process development and for material characterization

Tool supported model completion with support for design pattern application

Generative AI and large language models (LLMs), such Copilot and ChatGPT can complete code based on initial fragments written by a developer. They are integrated in software development environments such as VS code. Many papers analyse the advantages and limitations of these approaches for code generation, see for instance Besides some deficiencies, the produced code is often correct and the results that are getting increasingly better.

However, a surprisingly small amount of work has been done in the context of completion software models (for instance based on UML). The paper concludes that while the performance of the current LLMs for software modeling is still limited (in contrast to code generation), there is a need that (in contrast to code generation) we should adapt our model-based engineering practices to these new assistants and integrate these into MBSE methods and tools.

The integration of design-patterns is a complementary part of this work. Originally coming from building architecture, the term design patterns has been adopted in the software domain to capture a proven solution for a given problem along with its advantages and disadvantages. A bit later, the term anti-pattern has been proposed to identify patterns that are known not to work or having severe disadvantages. Thus, when proposing a completion, then assistant could explicitly reference an existing design pattern with its implications. The completion proposal can be based either on identified model fragments (including modeled requirements) or an explicit pattern selection. This thesis will explore the state-of-the-art of model completion with AI and design patterns and associated tool support. Up to now, little work is available on pattern formalization and the use in model tools. It will propose to identify the modelers intention, based on partial models. The task could be rule-based but should also explore machine-learning approaches. Implement a completion proposal in the context of a design tool, notably Papyrus SW designer. The solution will be evaluated.

Scatterometry measurement of the exposure focal length of photolithography tools

Since the late 2000s with the advent of 45nm CMOS nodes, the control of critical dimensions (CD) of the structures in the photolithography stage has become critical to the reliability of printed circuit boards. Optical photolithography remains the most economical and widespread technique for high volume production in the semiconductor industry. For this type of equipment, manufacturers have focused on increasing the numerical aperture of the exposure lens, reducing sources of optical aberrations and on metrology to ensure efficient monitoring of their machines. These developments were possible at the expense of the depth of field of the exposure. To avoid altering the images transferred to the photosensitive resins, and ultimately have a device failure, it is essential to give a value as accurate and precise as possible of this size. To meet the growing needs of process control and lithography tools required by the most advanced technologies, metrology techniques based on analysis of reflected signals are massively used. Although this methodology is well suited to current CMOS technologies (CMOS14nm and earlier), it is unlikely to address more advanced technologies, so other techniques must emerge, such as techniques based on the analysis of the diffracted signal (scatterometry).

Innovative dry etching process of exotic materials

The advantageous properties (electro-optical, - acoustic, -mechanical) of new materials such as Sc-doped ALN, LNO, LTO or KNN make them essential to meet the development needs of integrated optics, RF telecommunication and microsystems. The production of patterns with submicron dimensions with a significant etch rate (>100nm/min), a vertical profile and a reduced roughness of the pattern's sidewalls are the main goals of the thesis work so as to satisfy the performance criteria of the devices targeted at the application level.

Stabilisation of Perovskite photovoltaic devices by passivation with Metal-Organic Frameworks type materials

MOFs are a type of porous organic-inorganic hybrid material with interesting properties in terms of the passivation of defects in the perovskite and its stability, particularly versus light. For example:
- Direct effect of MOF components as passivation agents: Metal ions and organic ligands can passivate defects at the MOF/PK interface.
- Downconversion of incident radiation: Certain metals (such as europium) or ligands (with aromatic groups) can absorb high-energy radiation (typically violet/near-UV), then re-emit this energy in the form of lower-energy radiation or transmit it directly in a non-radiative manner to the perovskite by Förster resonance (or FRET). This protects the perovskite from high-energy photons, and therefore a priori improves light stability, with little energy loss.
The thesis work will focus on
- integrating MOFs into the perovskite layer, either as a surface treatment or as a mixture of suspensions
- Materials studies (in particular advanced studies using XPS and UPS)
- Favrication of single-junction devices and then tandem devices with silicon sub-cells
- Study of lifetime under illumination (continuous, cycling) with associated characterisations (electrical measurements, photoluminescence, etc.).

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