Quantitative analysis of spectroscopic low energy electron microscopy data: High-dynamic range imaging, drift...

Area of Science:

• Materials Science
• Surface Science
• Spectroscopy

Background:

• Low-energy electron microscopy (LEEM) combines real-space and reciprocal-space information for materials analysis.
• LEEM's energy-dependent measurements are often treated as simple images, underutilizing its potential.
• Complex materials systems require advanced analytical techniques for detailed characterization.

Purpose of the Study:

• To develop a measurement and data analysis approach for quantitative spectroscopic LEEM datasets.
• To achieve high lateral resolution in LEEM data acquisition and analysis.
• To enable intuitive presentation and interpretation of complex LEEM data.

Main Methods:

• Detector correction and adjustment for accurate reflectivity measurements over four orders of magnitude.
• A drift correction algorithm with sub-pixel accuracy for LEEM datasets with inverting contrast.
• Dimension reduction techniques, including cluster analysis, for summarizing spectroscopic features and identifying materials.

Main Results:

• Quantitative spectroscopic LEEM datasets with high lateral resolution were obtained.
• True reflectivity values were measured accurately across a wide intensity range.
• Dimension reduction and cluster analysis effectively summarized data and identified materials, demonstrated on graphene/SiC.

Conclusions:

• The developed approach transforms LEEM data into powerful, quantitative spectroscopic datasets.
• This method allows for accurate material identification and characterization within complex systems.
• A high-performance Python implementation is provided for broader accessibility and application.