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Abstract A variety of mechanisms are reported to play critical roles in contributing to the high carrier/electron mobility in oxide/SrTiO3(STO) heterostructures. By using La0.95Sr0.05TiO3(LSTO) epitaxially grown on different single crystal substrates (such as STO, GdScO3, LaAlO3, (LaAlO3)0.3(Sr2AlTaO6)0.7, and CeO2buffered STO) as the model systems, the formation of a conducting substrate surface layer (CSSL) on STO substrate is shown at relatively low growth temperature and high oxygen pressure (725 °C, 5 × 10–4 Torr), which contributes to the enhanced conductivity of the LSTO/STO heterostructures. Different from the conventional oxygen vacancy model, this work reveals that the formation of the CSSL occurs when growing an oxide layer (LSTO in this case) on STO, while neither annealing nor the growth of an Au layer alone at the exact same growth condition generates the CSSL in STO. It demonstrates that the oxide layer actively pulls oxygen from STO substrate at given growth conditions, leading to the formation of the CSSL. The observations emphasize the oxygen transfer across film/substrate interface during the synthesis of oxide heterostructures playing a critical role in functional properties.
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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/SrTiO3heterostructures 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/SrTiO3heterostructures 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.
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- Award ID(s):
- 1749440
- PAR ID:
- 10453464
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials Interfaces
- Volume:
- 8
- Issue:
- 3
- ISSN:
- 2196-7350
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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