Abstract
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.
Highlights
Identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon is crucial for accelerating technology development
Ever since Russell reported the first observation of the first-order inelastic Raman scattering in a Si single crystal[10], Raman spectroscopy has been intensively used to investigate the Si crystal structure[11], phonon dispersion[12], electronic states[13], local stress and strain[14,15], and thermal properties[16], which are integral to the performances of silicon-based devices
Upon optimization of the polarization conditions, Sokolov et al improved the ratio of the near-field Raman intensity and far-field Raman intensity by more than one order of magnitude[24], being able to carry out the nano-Raman analysis of the crystal Si at a ~20 nm lateral resolution
Summary
Identifying the local structural heterogeneity of complex, disordered amorphous materials such as amorphous silicon is crucial for accelerating technology development. To explore relationships among all the high dimensional TERS spectra detailing the a-Si local structures, we first construct the nearest neighbor (NN) graph by calculating pairwise distances.
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