Leuven | More than two weeks ago
In recent years, the potential of 2D
materials, in particular semiconducting transition metal dichalcogenides (MX2),
has emerged as a promising alternative to Si for driving the logic scaling
roadmap forward. However, integrating
these materials into logic devices requires a fundamental understanding of their
performance and variability. One key parameter is carrier mobility which
directly influences transistor performance and is impacted by defects and strain
within the material. As PhD candidate, you will use cutting-edge electron
microscopy techniques to dive deep in the world of MX2 materials, unraveling
the impact of these local variations on material properties and on device performance.
Electron microscopy, particularly in transmission mode, is the primary method for directly validating hypotheses about material and device characteristics thanks to its higher resolution compared to conventional techniques. Detector innovation has enabled rapid acquisition of multi-dimensional data such as 4DSTEM which capture a diffraction pattern at each beam position. This results in large four-dimensional datasets from which a wide range of information such as orientation, phase, thickness, stacking, strain, etc., can be extracted. Consequently, there is a growing need for fast and automated processing of such large datasets to obtain quantitative insights into material disorder, representing a rapid growing area of research.
Key aspects of this PhD
In this PhD the student will have the opportunity to collaborate with different groups internal and/or external to imec to investigate the following:
Required background:
Master in Physics/ Materials Science/Nanotechnology/Computer Science/Machine Learning
Good expertise in programming (python, C++) and a basic understanding of algorithms and image processing
Experience with electron microscopy is a plus
Knowledge in machine learning is desirable
Type of work: Literature review: 20%, Experiments: 40%, Data analysis and simulations: 40%
Supervisor: Claudia Fleischmann
Daily advisor: Paola Favia, Ankit Nalin Mehta
Required background: Physics/ Materials Science/Nanotechnology/Computer Science/Machine Learning
Type of work: Literature review: 20%, Experiments: 40%, Data analysis and simulations: 40%
Supervisor: Claudia Fleischmann
Daily advisor: Paola Favia, Ankit Nalin Mehta
The reference code for this position is 2024-165. Mention this reference code on your application form.