Dataset Information

Deep Learning-Assisted Quantification of Atomic Dopants and Defects in 2D Materials.

ABSTRACT: Atomic dopants and defects play a crucial role in creating new functionalities in 2D transition metal dichalcogenides (2D TMDs). Therefore, atomic-scale identification and their quantification warrant precise engineering that widens their application to many fields, ranging from development of optoelectronic devices to magnetic semiconductors. Scanning transmission electron microscopy with a sub-Å probe has provided a facile way to observe local dopants and defects in 2D TMDs. However, manual data analytics of experimental images is a time-consuming task, and often requires subjective decisions to interpret observed signals. Therefore, an approach is required to automate the detection and classification of dopants and defects. In this study, based on a deep learning algorithm, fully convolutional neural network that shows a superior ability of image segmentation, an efficient and automated method for reliable quantification of dopants and defects in TMDs is proposed with single-atom precision. The approach demonstrates that atomic dopants and defects are precisely mapped with a detection limit of ≈1 × 10¹² cm^-2 , and with a measurement accuracy of ≈98% for most atomic sites. Furthermore, this methodology is applicable to large volume of image data to extract atomic site-specific information, thus providing insights into the formation mechanisms of various defects under stimuli.

SUBMITTER: Yang SH

PROVIDER: S-EPMC8373156 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Deep Learning-Assisted Quantification of Atomic Dopants and Defects in 2D Materials.

Yang Sang-Hyeok SH Choi Wooseon W Cho Byeong Wook BW Agyapong-Fordjour Frederick Osei-Tutu FO Park Sehwan S Yun Seok Joon SJ Kim Hyung-Jin HJ Han Young-Kyu YK Lee Young Hee YH Kim Ki Kang KK Kim Young-Min YM

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20210603 16

Atomic dopants and defects play a crucial role in creating new functionalities in 2D transition metal dichalcogenides (2D TMDs). Therefore, atomic-scale identification and their quantification warrant precise engineering that widens their application to many fields, ranging from development of optoelectronic devices to magnetic semiconductors. Scanning transmission electron microscopy with a sub-Å probe has provided a facile way to observe local dopants and defects in 2D TMDs. However, manual da ...[more]

PMID: 34081415

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Dataset Information

Deep Learning-Assisted Quantification of Atomic Dopants and Defects in 2D Materials.

Publications

Deep Learning-Assisted Quantification of Atomic Dopants and Defects in 2D Materials.

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