论文标题
通过分子束外延生长的钙钛矿氧化物的机器学习分析
Machine Learning Analysis of Perovskite Oxides Grown by Molecular Beam Epitaxy
论文作者
论文摘要
反射高能电子衍射（Rheed）是一种无处不在的原位分子束外延（MBE）表征工具。尽管Rheed可以是确定晶体表面结构的强大手段，但在生长过程中，它通常用作离散时间间隔的静态定性表面表征方法。使用原理成分分析（PCA）和K-均值聚类，对在整个MBE增长过程中收集的Rheed数据进行了完整分析，以检查从Rheed数据分组的时间簇中发生的重要边界，并识别具有统计学意义的模式。此过程适用于来自同性恋srtio $ _ {3} $增长的数据，杂物性srtio $ _ {3} $在丑闻基质上生长，basno $ _ {3} $在srtio $ _ {3} $ substrates上生长的胶片，并在srtio $ _ {3} $ substrate of lanio of fillate of lanio of thrate和lanio of。 laalo $ _ {3} $基质。该分析可能会在精确的时间和生长过程中对生长模式的表面演变和过渡提供更多见解，并且整个Rheed图像序列的视频档案可能能够提供对生长过程和膜质量的更多见识和控制。
Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and k-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO$_{3}$ growths, heteroepitaxial SrTiO$_{3}$ grown on scandate substrates, BaSnO$_{3}$ films grown on SrTiO$_{3}$ substrates, and LaNiO$_{3}$ films grown on LaAlO$_{3}$ substrates. This analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control over growth processes and film quality.
