Protocol for Clustering 4DSTEM Data for Phase Differentiation in Glasses
This work addresses the problem of characterizing nanoscale variations in phase-change materials for non-volatile memory applications, offering a framework to correlate local features, though it is incremental as it applies existing clustering methods to new data.
The researchers tackled the challenge of resolving nanoscale compositional and structural heterogeneity in phase-change materials like Ge-Sb-Te alloys by applying unsupervised machine learning to 4D-STEM data, identifying four distinct clusters with specific chemical and structural variations.
Phase-change materials (PCMs) such as Ge-Sb-Te alloys are widely used in non-volatile memory applications due to their rapid and reversible switching between amorphous and crystalline states. However, their functional properties are strongly governed by nanoscale variations in composition and structure, which are challenging to resolve using conventional techniques. Here, we apply unsupervised machine learning to 4-dimensional scanning transmission electron microscopy (4D-STEM) data to identify compositional and structural heterogeneity in Ge-Sb-Te. After preprocessing and dimensionality reduction with principal component analysis (PCA), cluster validation was performed with t-SNE and UMAP, followed by k-means clustering optimized through silhouette scoring. Four distinct clusters were identified which were mapped back to the diffraction data. Elemental intensity histograms revealed chemical signatures change across clusters, oxygen and germanium enrichment in Cluster 1, tellurium in Cluster 2, antimony in Cluster 3, and germanium again in Cluster 4. Furthermore, averaged diffraction patterns from these clusters confirmed structural variations. Together, these findings demonstrate that clustering analysis can provide a powerful framework for correlating local chemical and structural features in PCMs, offering deeper insights into their intrinsic heterogeneity.