Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan.
Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan.
Sci Data. 2023 Feb 14;10(1):91. doi: 10.1038/s41597-023-02004-6.
Defects to popular two-dimensional (2D) transition metal dichalcogenides (TMDs) seriously lower the efficiency of field-effect transistor (FET) and depress the development of 2D materials. These atomic defects are mainly identified and researched by scanning tunneling microscope (STM) because it can provide precise measurement without harming the samples. The long analysis time of STM for locating defects in images has been solved by combining feature detection with convolutional neural networks (CNN). However, the low signal-noise ratio, insufficient data, and a large amount of TMDs members make the automatic defect detection system hard to be applied. In this study, we propose a deep learning-based atomic defect detection framework (DL-ADD) to efficiently detect atomic defects in molybdenum disulfide (MoS) and generalize the model for defect detection in other TMD materials. We design DL-ADD with data augmentation, color preprocessing, noise filtering, and a detection model to improve detection quality. The DL-ADD provides precise detection in MoS (F2-scores is 0.86 on average) and good generality to WS (F2-scores is 0.89 on average).
二维（2D）过渡金属二卤化物（TMD）的缺陷严重降低了场效应晶体管（FET）的效率，并抑制了 2D 材料的发展。这些原子缺陷主要通过扫描隧道显微镜（STM）来识别和研究，因为它可以在不损坏样品的情况下提供精确的测量。STM 用于在图像中定位缺陷的长分析时间已通过将特征检测与卷积神经网络（CNN）结合来解决。然而，低信噪比、数据不足和大量 TMD 成员使得自动缺陷检测系统难以应用。在这项研究中，我们提出了一种基于深度学习的原子缺陷检测框架（DL-ADD），以有效地检测二硫化钼（MoS）中的原子缺陷，并推广该模型用于其他 TMD 材料的缺陷检测。我们设计了 DL-ADD，采用了数据增强、颜色预处理、噪声过滤和检测模型，以提高检测质量。DL-ADD 在 MoS 中提供了精确的检测（平均 F2 得分为 0.86），并且在 WS 中具有良好的通用性（平均 F2 得分为 0.89）。
