Dimensional Stacking for Machine Learning in ToF‐SIMS Analysis of Heterostructures

Abstract 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.