通过卷积神经网络对硫化物薄膜和纳米结构生长中的RHEED图案进行分类
RHEED pattern classification by a convolutional neural network for the growth of chalcogenide thin films and nanostructures
AI总结：
本研究利用卷积神经网络对硫化物薄膜生长中的RHEED图案进行分类，实现了高准确率的自动识别，为实时控制纳米结构生长提供新方法。
摘要：
反射高能电子衍射（RHEED）在分子束外延、脉冲激光沉积和溅射等原位表征中的应用至关重要。尽管对晶体对称性和形貌敏感，它被广泛用于确定薄膜的生长模式。然而，RHEED图案的分析依赖于熟练专家，因此难以实时纳入生长策略。机器学习（ML）过程，特别是卷积神经网络（CNNs），为实时RHEED图案识别提供了独特的机会。在本研究中，我们开发了一个CNN模型，能够准确分类四种常见的、不同的RHEED图案，这些图案出现在硫化物薄膜生长中。其在单次运行中达到94.9%的准确率，平均20次种子测试中为91.2%。我们的网络能够区分三种常见的生长模式，即Volmer Weber、Stransky-Krastanov和Frank-van der Merwe的成核，可能使未来能够通过RHEED数据自动控制基底温度和遮挡板控制。该网络是材料无关的，能够以超过98%的准确率区分VW过程，但其在正确分类粗糙化和Stransky-Krastanov生长起始方面的能力有所限制。我们的发现表明，即使在没有详细生长化学知识的情况下，ML技术也可以成功实施，为实时将ML应用于控制纳米结构成核和薄膜形貌提供了途径。
英文摘要：
The use of reflection high energy electron diffraction (RHEED) plays a critical role for in-situ characterization in molecular beam epitaxy, pulsed laser deposition and sputtering. While sensitive to crystal symmetries and morphology, it is used ubiquitously to determine the growth modes of thin films. However, analysis of RHEED patterns depends on skilled experts and is therefore difficult to incorporate into the growth strategy in real-time. The development of machine learning (ML) processes, specifically convolutional neural networks (CNNs), presents a unique opportunity towards real-time RHEED pattern recognition. In this study, we develop a CNN model that can accurately classify four common and distinct RHEED patterns present in chalcogenide thin film growth. Its reached accuracy reached 94.9% for single run and 91.2% when averaged over 20 seeds. Our network is able to distinguish the nucleation of three common growth modes encountered in epitaxy, namely Volmer Weber, Stransky-Krastanov and Frank-van der Merwe, potentially enabling future automation of substrate temperature and shutter control informed by RHEED data. The network is material-agnostic and distinguishes the VW process with greater than 98% accuracy but is somewhat more limited in its ability to properly classify roughening and the initiation of Stransky-Krastanov growth. Our findings show that ML techniques can be successfully implemented even in cases where there is no detailed knowledge of growth chemistry providing an avenue towards real-time incorporation of ML to control nanostructure nucleation and thin film morphology.
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