Distilling nanoscale heterogeneity of amorphous silicon using tip- enhanced Raman spectroscopy (TERS) via multiresolution manifold learning

G Yang and X Li and YQ Cheng and MC Wang and D Ma and AP Sokolov and SV Kalinin and GM Veith and J Nanda, NATURE COMMUNICATIONS, 12, 578 (2021).

DOI: 10.1038/s41467-020-20691-2

Accurately identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon is crucial for accelerating technology development. However, short-range atomic ordering quantification and nanoscale spatial resolution over a large area on a-Si have remained major challenges and practically unexplored. We resolve phonon vibrational modes of a-Si at a lateral resolution of <60 nm by tip-enhanced Raman spectroscopy. To project the high dimensional TERS imaging to a two-dimensional manifold space and categorize amorphous silicon structure, we developed a multiresolution manifold learning algorithm. It allows for quantifying average Si-Si distortion angle and the strain free energy at nanoscale without a human-specified physical threshold. The multiresolution feature of the multiresolution manifold learning allows for distilling local defects of ultra-low abundance (< 0.3%), presenting a new Raman mode at finer resolution grids. This work promises a general paradigm of resolving nanoscale structural heterogeneity and updating domain knowledge for highly disordered materials. Short range atomic ordering quantification and nanoscale spatial resolution over a large area for amorphous materials is crucial for accelerating technology development but remain challenges. Here, the authors explore nanoscale heterogeneity of amorphous silicon by tip-enhanced Raman spectroscopy via multiresolution manifold learning.

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