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

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dc.contributor.authorYang, Sang-Hyeok-
dc.contributor.authorChoi, Wooseon-
dc.contributor.authorByeong Wook Cho-
dc.contributor.authorAgyapong-Fordjour, Frederick Osei-Tutu-
dc.contributor.authorSehwan Park-
dc.contributor.authorSeok Joon Yun-
dc.contributor.authorKim, Hyung-Jin-
dc.contributor.authorHan, Young-Kyu-
dc.contributor.authorYoung Hee Lee-
dc.contributor.authorKi Kang Kim-
dc.contributor.authorYoung-Min Kim-
dc.date.accessioned2021-10-12T07:50:20Z-
dc.date.available2021-10-12T07:50:20Z-
dc.date.created2021-07-07-
dc.date.issued2021-08-
dc.identifier.issn2198-3844-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/10406-
dc.description.abstract© 2021 The Authors. Advanced Science published by Wiley-VCH GmbHAtomic 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.-
dc.language영어-
dc.publisherWiley-VCH Verlag-
dc.titleDeep Learning-Assisted Quantification of Atomic Dopants and Defects in 2D Materials-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000657449700001-
dc.identifier.scopusid2-s2.0-85107022651-
dc.identifier.rimsid75982-
dc.contributor.affiliatedAuthorByeong Wook Cho-
dc.contributor.affiliatedAuthorSehwan Park-
dc.contributor.affiliatedAuthorSeok Joon Yun-
dc.contributor.affiliatedAuthorYoung Hee Lee-
dc.contributor.affiliatedAuthorKi Kang Kim-
dc.contributor.affiliatedAuthorYoung-Min Kim-
dc.identifier.doi10.1002/advs.202101099-
dc.identifier.bibliographicCitationAdvanced Science, v.8, no.16-
dc.relation.isPartOfAdvanced Science-
dc.citation.titleAdvanced Science-
dc.citation.volume8-
dc.citation.number16-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryNanoscience & Nanotechnology-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordPlusGENERALIZED GRADIENT APPROXIMATION-
dc.subject.keywordPlusPHASE-TRANSITION-
dc.subject.keywordPlusEVOLUTION-
dc.subject.keywordPlusSPARSE-
dc.subject.keywordPlusMOTE2-
dc.subject.keywordAuthor2D transition metal dichalcogenides-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthordynamic STEM analysis-
dc.subject.keywordAuthorpoint defects-
dc.subject.keywordAuthorscanning transmission electron microscopy-
Appears in Collections:
Center for Integrated Nanostructure Physics(나노구조물리 연구단) > 1. Journal Papers (저널논문)
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