Clustering

Analytics -> Video Clustering

Once a file has been processed and status reads Success, select the file to view clustering visualizations

Selecting a file

You have several options to search or filter your files to make it easier to find the one you want

• Filter by target material

• Search by file name

• Sort by date or other parameter

RHEED Clusters plot

After selecting a file the the RHEED Clusters plot will automatically populate below. This displays a timeseries plotting cluster groupings, cluster uncertainty, and specular intensity.

Cluster Compare

With a file selected, you can inspect and compare the diffraction patterns of each cluster. Use the scrolling gallery below the RHEED Clusters plot to select which cluster you wish to view. Additional metrics are displayed for each cluster including, oscillation period, Streak-to-Spot Ratio, and spot count.

Under the hood:

Clustering

Clustering algorithms are applied to identify and group statistically similar frames within the RHEED recording. This clustering is done without knowledge of the frame sequence (time order) providing robust statistical grouping.

Clusters are used to identify changes in the growth phase as they signal significant changes in the diffraction pattern's evolution.

Machine learning analysis of perovskite oxides grown by molecular beam epitaxy (Sydney, Et Al.)

Engineering ordered arrangements of oxygen vacancies at the surface of superconducting La2CuO4 thin films (Suyolcu, Et Al.)

Skill-Agnostic analysis of reflection high-energy electron diffraction patterns for Si(111) surface superstructures using machine learning (Asako Yoshinari, Et Al.)

Streak-to-Spot Ratio

This value quantifies whether you are closer to an island-like growth mode (high streak/spot ratio) or a layer by layer growth mode (low streak/spot ratio)

Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping (Liang, Et Al.)