Dennler Nik, Foncubierta-Rodriguez Antonio, Neupert Titus, Sousa Marilyne
IBM Research Europe - Zurich, Rüschlikon, 8803, Switzerland; University of Zurich and ETH Zurich, Institute of Neuroinformatics, Zurich, 8057, Switzerland.
IBM Research Europe - Zurich, Rüschlikon, 8803, Switzerland.
Micron. 2021 Jul;146:103069. doi: 10.1016/j.micron.2021.103069. Epub 2021 May 3.
Controlling crystalline material defects is crucial, as they affect properties of the material that may be detrimental or beneficial for the final performance of a device. Defect analysis on the sub-nanometer scale is enabled by high-resolution scanning transmission electron microscopy (HRSTEM), where the identification of defects is currently carried out based on human expertise. However, the process is tedious, highly time consuming and, in some cases, yields ambiguous results. Here we propose a semi-supervised machine learning method that assists in the detection of lattice defects from atomic resolution HRSTEM images. It involves a convolutional neural network that classifies image patches as defective or non-defective, a graph-based heuristic that chooses one non-defective patch as a model, and finally an automatically generated convolutional filter bank, which highlights symmetry breaking such as stacking faults, twin defects and grain boundaries. Additionally, we suggest a variance filter to segment amorphous regions and beam defects. The algorithm is tested on III-V/Si crystalline materials and successfully evaluated against different metrics and a baseline approach, showing promising results even for extremely small training data sets and for noise compromised images. By combining the data-driven classification generality, robustness and speed of deep learning with the effectiveness of image filters in segmenting faulty symmetry arrangements, we provide a valuable open-source tool to the microscopist community that can streamline future HRSTEM analyses of crystalline materials.
控制晶体材料缺陷至关重要，因为它们会影响材料的性能，而这些性能可能对器件的最终性能有害或有益。高分辨率扫描透射电子显微镜（HRSTEM）能够实现亚纳米尺度的缺陷分析，目前缺陷的识别是基于人类专业知识进行的。然而，这个过程繁琐、耗时极长，而且在某些情况下，结果并不明确。在此，我们提出一种半监督机器学习方法，用于协助从原子分辨率的HRSTEM图像中检测晶格缺陷。它包括一个将图像块分类为有缺陷或无缺陷的卷积神经网络、一种基于图的启发式方法，该方法选择一个无缺陷的图像块作为模型，以及最后一个自动生成的卷积滤波器组，它能突出诸如堆垛层错、孪晶缺陷和晶界等对称性破坏。此外，我们还提出了一种方差滤波器来分割非晶区域和束流缺陷。该算法在III-V/Si晶体材料上进行了测试，并针对不同指标和一种基线方法成功进行了评估，即使对于极小的训练数据集和噪声干扰的图像也显示出了有前景的结果。通过将数据驱动的深度学习分类的通用性、鲁棒性和速度与图像滤波器在分割错误对称排列方面的有效性相结合，我们为显微镜学家群体提供了一个有价值的开源工具，它可以简化未来对晶体材料的HRSTEM分析。
