PhD position | Exploring 2D Material Performance and Variability through Advanced Data Analytics in Electron Microscopy

Future Transistor Insights through Advanced Data Analytics in Electron Microscopy

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:

• Quantification of defects and strain via electron microscopy techniques, optimization of data acquisition and machine learning aided data analysis.
• Correlation with other material and device characteristics.
• Reporting and publishing of results in leading scientific journals and conferences.

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.