Yang Guang, Li Xin, Cheng Yongqiang, Wang Mingchao, Ma Dong, Sokolov Alexei P, Kalinin Sergei V, Veith Gabriel M, Nanda Jagjit
Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
State Key Laboratory of Green Chemical Engineering and Industrial Catalysis, Sinopec Shanghai Research Institute of Petrochemical Technology, 1658 Pudong Beilu, Shanghai, PR, 201208, China.
Nat Commun. 2021 Jan 25;12(1):578. 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.
准确识别诸如非晶硅等复杂无序非晶材料的局部结构异质性对于加速技术发展至关重要。然而，在大面积的非晶硅上进行短程原子有序量化和纳米级空间分辨率仍然是主要挑战，并且实际上尚未得到探索。我们通过针尖增强拉曼光谱以小于60纳米的横向分辨率解析了非晶硅的声子振动模式。为了将高维针尖增强拉曼光谱成像投影到二维流形空间并对非晶硅结构进行分类，我们开发了一种多分辨率流形学习算法。它能够在无需人为指定物理阈值的情况下，在纳米尺度上量化平均Si-Si畸变角和无应变自由能。多分辨率流形学习的多分辨率特征能够提取超低丰度（<0.3%）的局部缺陷，在更精细的分辨率网格上呈现新的拉曼模式。这项工作有望为解决纳米级结构异质性和更新高度无序材料的领域知识提供一种通用范式。
