STEM image analysis based on deep learning: identification of vacancy defects and polymorphs of ${MoS_2}$
Authors:
Kihyun Lee,
Jinsub Park,
Soyeon Choi,
Yangjin Lee,
Sol Lee,
Joowon Jung,
Jong-Young Lee,
Farman Ullah,
Zeeshan Tahir,
Yong Soo Kim,
Gwan-Hyoung Lee,
Kwanpyo Kim
Abstract:
Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional…
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Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying sulfur vacancies and polymorph types of ${MoS_2}$ from atomic resolution STEM images. Efficient models are achieved based on training with simulated images in the presence of different levels of noise, aberrations, and carbon contamination. The accuracy of the FCN models toward extensive experimental STEM images is comparable to that of careful hands-on analysis. Our work provides a guideline on best practices to train a deep learning model for STEM image analysis and demonstrates FCN's application for efficient processing of a large volume of STEM data.
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Submitted 9 June, 2022;
originally announced June 2022.
Large-Scale Conformal Growth of Atomic-Thick MoS2 for Highly Efficient Photocurrent Generation
Authors:
Tri Khoa Nguyen,
Anh Duc Nguyen,
Chinh Tam Le,
Farman Ullah,
Kyo-in Koo,
Eunah Kim,
Dong-Wook Kim,
Joon I. Jang,
Yong Soo Kim
Abstract:
Controlling the interconnection of neighboring seeds (nanoflakes) to full coverage of the textured substrate is the main challenge for the large-scale conformal growth of atomic-thick transition metal dichalcogenides by chemical vapor deposition. Herein, we report on a controllable method for the conformal growth of monolayer MoS2 on not only planar but also micro- and nano-rugged SiO2/Si substrat…
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Controlling the interconnection of neighboring seeds (nanoflakes) to full coverage of the textured substrate is the main challenge for the large-scale conformal growth of atomic-thick transition metal dichalcogenides by chemical vapor deposition. Herein, we report on a controllable method for the conformal growth of monolayer MoS2 on not only planar but also micro- and nano-rugged SiO2/Si substrates via metal-organic chemical vapor deposition. The continuity of monolayer MoS2 on the rugged surface is evidenced by scanning electron microscopy, cross-section high-resolution transmission electron microscopy, photoluminescence (PL) mapping, and Raman mapping. Interestingly, the photo-responsivity (~254.5 mA/W) of as-grown MoS2 on the nano-rugged substrate exhibits 59 times higher than that of the planar sample (4.3 mA/W) under a small applied bias of 0.1 V. This value is record high when compared with all previous MoS2-based photocurrent generation under low or zero bias. Such a large enhancement in the photo-responsivity arises from a large active area for light-matter interaction and local strain for PL quenching, where the latter effect is the key factor and unique in the conformally grown monolayer on the nano-rugged surface. The result is a step toward the batch fabrication of modern atomic-thick optoelectronic devices.
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Submitted 27 July, 2018;
originally announced July 2018.