Advanced characterization of defects generated by technological processes for high-performance infrared imaging
This thesis falls within the field of cooled infrared detectors. The CEA-LETI-MINATEC Infrared Laboratory specializes in the design and manufacture of infrared camera prototypes used in defense, astronomy, environmental monitoring, and satellite meteorology.
In this context of high-performance imaging, it is crucial to ensure optimal detector quality. However, manufacturing processes can introduce defects that can degrade sensor performance. Understanding and controlling these defects is essential to increase reliability and optimize processes.
The objective of the thesis is to identify and precisely characterize these defects using cutting-edge techniques, rarely combined, such as Laue microdiffraction and FIB-SEM nanotomography, enabling structural analysis at different scales. By linking the nature and origin of defects to manufacturing processes and quantifying their impact on performance, the doctoral student will contribute directly to improving the reliability and efficiency of next-generation infrared sensors.
The doctoral student will join a team covering the entire detector manufacturing chain and will actively participate in the development (LETI clean room) and structural characterization (CEA-Grenoble platform, advanced techniques) of samples. He/she will also be involved in electro-optical characterization in partnership with the Cooled Infrared Imaging Laboratory (LIR), which specializes in detailed analysis of active materials at cryogenic temperatures.
Artificial Intelligence for the Modeling and Topographic Analysis of Electronic Chips
The inspection of wafer surfaces is critical in microelectronics to detect defects affecting chip quality. Traditional methods, based on physical models, are limited in accuracy and computational efficiency. This thesis proposes using artificial intelligence (AI) to characterize and model wafer topography, leveraging optical interferometry techniques and advanced AI models.
The goal is to develop AI algorithms capable of predicting topographical defects (erosion, dishing) with high precision, using architectures such as convolutional neural networks (CNN), generative models, or hybrid approaches. The work will include optimizing models for fast inference and robust generalization while reducing manufacturing costs.
This project aligns with efforts to improve microfabrication processes, with potential applications in the semiconductor industry. The expected results will contribute to a better understanding of surface defects and the optimization of production processes.
Development of 4D-STEM with variable tilts
The development of 4D-STEM (Scanning Transmission Electron Microscopy) has profoundly transformed transmission electron microscopy (TEM) by enabling the simultaneous recording of spatial (2D) and diffraction (2D) information at each probe position. These so-called “4D” datasets make it possible to extract a wide variety of virtual contrasts (bright-field imaging, annular dark-field imaging, ptychography, strain and orientation mapping) with nanometer-scale spatial resolution.
In this context, 4D-STEM with variable beam tilts (4D-STEMiv) is an emerging approach that involves sequentially acquiring electron diffraction patterns for different incident beam tilts. Conceptually similar to precession electron diffraction (PED), this method offers greater flexibility and opens new possibilities: improved signal-to-noise ratio, faster two-dimensional imaging at higher spatial resolution, access to three-dimensional information (orientation, strain, phase), and optimized coupling with spectroscopic analyses (EELS, EDX).
The development of 4D-STEMiv thus represents a major methodological challenge for the structural and chemical characterization of advanced materials, particularly in the fields of nanostructures, two-dimensional materials, and ferroelectric systems.