Dimensional Stacking for Machine Learning in ToF-SIMS Analysis of Heterostructures
Dimensional Stacking for Machine Learning in ToF-SIMS Analysis of Heterostructures
复制标题
DOI：
10.1002/admi.202001648
复制
发表时间：
2020-12-10
影响因子：
5.4
通讯作者：
Sehirlioglu, Alp
中科院分区：
文献类型：
--
作者：
Abbasi, Kevin;Smith, Hugh;Sehirlioglu, Alp
从光谱成像技术获得的多维数据集的输出提供了适合于机器学习技术的大数据，以阐明定义样本中最大方差的物理和化学属性。在这里，最近提出的技术的尺寸堆叠应用于获得累积深度超过几个LaAlO 3/SrTiO 3异质结构具有不同的厚度。通过降维技术，通过非负矩阵分解（NMF）和主成分分析（PCA），它表明，维度堆叠提供了更强大的统计和共识，同时仍然能够分离不同的样本的不同参数。比较了堆积和非堆积样品以及降维技术的结果。应用于四个不同厚度的LaAlO 3/SrTiO 3异质结构，NMF能够分离1）表面和薄膜终端; 2）薄膜; 3）界面位置;和4）衬底属性彼此接近完美的共识。然而，PCA导致与衬底相关的数据的丢失。
Output from multidimensional datasets obtained from spectroscopic imaging techniques provides large data suitable for machine learning techniques to elucidate physical and chemical attributes that define the maximum variance in the specimens. Here, a recently proposed technique of dimensional stacking is applied to obtain a cumulative depth over several LaAlO3/SrTiO3 heterostructures with varying thicknesses. Through dimensional reduction techniques via non-negative matrix factorization (NMF) and principal component analysis (PCA), it is shown that dimensional stacking provides much more robust statistics and consensus while still being able to separate different specimens of varying parameters. The results of stacked and unstacked samples as well as the dimensional reduction techniques are compared. Applied to four LaAlO3/SrTiO3 heterostructures with varying thicknesses, NMF is able to separate 1) surface and film termination; 2) film; 3) interface position; and 4) substrate attributes from each other with near perfect consensus. However, PCA results in the loss of data related to the substrate.