Citation

BibTex format

@article{Dong:2021:10.1021/acsnano.0c09685,
author = {Dong, X and Li, H and Jiang, Z and Gruenleitner, T and Gueler, I and Dong, J and Wang, K and Koehler, MH and Jakobi, M and Menze, BH and Yetisen, AK and Sharp, ID and Stier, A and Finley, JJ and Koch, AW},
doi = {10.1021/acsnano.0c09685},
journal = {ACS Nano},
pages = {3139--3151},
title = {3D deep learning enables accurate layer mapping of 2D materials},
url = {http://dx.doi.org/10.1021/acsnano.0c09685},
volume = {15},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Layered, two-dimensional (2D) materials are promising for next-generation photonics devices. Typically, the thickness of mechanically cleaved flakes and chemical vapor deposited thin films is distributed randomly over a large area, where accurate identification of atomic layer numbers is time-consuming. Hyperspectral imaging microscopy yields spectral information that can be used to distinguish the spectral differences of varying thickness specimens. However, its spatial resolution is relatively low due to the spectral imaging nature. In this work, we present a 3D deep learning solution called DALM (deep-learning-enabled atomic layer mapping) to merge hyperspectral reflection images (high spectral resolution) and RGB images (high spatial resolution) for the identification and segmentation of MoS2 flakes with mono-, bi-, tri-, and multilayer thicknesses. DALM is trained on a small set of labeled images, automatically predicts layer distributions and segments individual layers with high accuracy, and shows robustness to illumination and contrast variations. Further, we show its advantageous performance over the state-of-the-art model that is solely based on RGB microscope images. This AI-supported technique with high speed, spatial resolution, and accuracy allows for reliable computer-aided identification of atomically thin materials.
AU  - Dong,X
AU  - Li,H
AU  - Jiang,Z
AU  - Gruenleitner,T
AU  - Gueler,I
AU  - Dong,J
AU  - Wang,K
AU  - Koehler,MH
AU  - Jakobi,M
AU  - Menze,BH
AU  - Yetisen,AK
AU  - Sharp,ID
AU  - Stier,A
AU  - Finley,JJ
AU  - Koch,AW
DO  - 10.1021/acsnano.0c09685
EP  - 3151
PY  - 2021///
SN  - 1936-0851
SP  - 3139
TI  - 3D deep learning enables accurate layer mapping of 2D materials
T2  - ACS Nano
UR  - http://dx.doi.org/10.1021/acsnano.0c09685
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000623061800096&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://pubs.acs.org/doi/10.1021/acsnano.0c09685
VL  - 15
ER  -
Download