Open AccessDeep Learning‐Assisted Quantification of Atomic Dopants and Defects in 2D Materials
Author(s) -
Yang SangHyeok,
Choi Wooseon,
Cho Byeong Wook,
AgyapongFordjour Frederick OseiTutu,
Park Sehwan,
Yun Seok Joon,
Kim HyungJin,
Han YoungKyu,
Lee Young Hee,
Kim Ki Kang,
Kim YoungMin
Publication year - 2021
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202101099
Subject(s) - dopant , atomic units , materials science , convolutional neural network , computer science , nanotechnology , semiconductor , artificial intelligence , atom (system on chip) , doping , optoelectronics , physics , embedded system , quantum mechanics
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 × 10 12 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.