Dataset Information

Toward the use of CVD-grown MoS₂ nanosheets as field-emission source.

ABSTRACT: Densely populated edge-terminated vertically aligned two-dimensional MoS₂ nanosheets (NSs) with thicknesses ranging from 5 to 20 nm were directly synthesized on Mo films deposited on SiO₂ by sulfurization. The quality of the obtained NSs was analyzed by scanning electron and transmission electron microscopy, and Raman and X-ray photoelectron spectroscopy. The as-grown NSs were then successfully transferred to the substrates using a wet chemical etching method. The transferred NSs sample showed excellent field-emission properties. A low turn-on field of 3.1 V/?m at a current density of 10 µA/cm² was measured. The low turn-on field is attributed to the morphology of the NSs exhibiting vertically aligned sheets of MoS₂ with sharp and exposed edges. Our findings show that the fabricated MoS₂ NSs could have a great potential as robust high-performance electron-emitter material for various applications such as microelectronics and nanoelectronics, flat-panel displays and electron-microscopy emitter tips.

SUBMITTER: Deokar G

PROVIDER: S-EPMC6009318 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Toward the use of CVD-grown MoS2 nanosheets as field-emission source.

Deokar Geetanjali G Deokar Geetanjali G Rajput Nitul S NS Li Junjie J Deepak Francis Leonard FL Ou-Yang Wei W Reckinger Nicolas N Bittencourt Carla C Colomer Jean-Francois JF Jouiad Mustapha M

Beilstein journal of nanotechnology 20180607

Densely populated edge-terminated vertically aligned two-dimensional MoS2 nanosheets (NSs) with thicknesses ranging from 5 to 20 nm were directly synthesized on Mo films deposited on SiO2 by sulfurization. The quality of the obtained NSs was analyzed by scanning electron and transmission electron microscopy, and Raman and X-ray photoelectron spectroscopy. The as-grown NSs were then successfully transferred to the substrates using a wet chemical etching method. The transferr ...[more]

PMID: 29977702

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Project description:BackgroundIn Complementary Metal-Oxide Semiconductor (CMOS) technology, scaling down has been a key strategy to improve chip performance and reduce power losses. However, challenges such as sub-threshold leakage and gate leakage, resulting from short-channel effects, contribute to an increase in distributed static power. Two-dimensional transition metal dichalcogenides (2D TMDs) emerge as potential solutions, serving as channel materials with steep sub-threshold swings and lower power consumption. However, the production and development of these 2-dimensional materials require some time-consuming tasks. In order to employ them in different fields, including chip technology, it is crucial to ensure that their production meets the required standards of quality and uniformity; in this context, deep learning techniques show significant potential.MethodsThis research introduces a transfer learning-based deep convolutional neural network (CNN) to classify chemical vapor deposition (CVD) grown molybdenum disulfide (MoS2) flakes based on their uniformity or the occurrence of defects affecting electronic properties. Acquiring and labeling a sufficient number of microscope images for CNN training may not be realistic. To address this challenge, artificial images were generated using Fresnel equations to pre-train the CNN. Subsequently, accuracy was improved through fine-tuning with a limited set of real images.ResultsThe proposed transfer learning-based CNN method significantly improved all measurement metrics with respect to the ordinary CNNs. The initial CNN, trained with limited data and without transfer learning, achieved 68% average accuracy for binary classification. Through transfer learning and artificial images, the same CNN achieved 85% average accuracy, demonstrating an average increase of approximately 17%. While this study specifically focuses on MoS2 structures, the same methodology can be extended to other 2-dimensional materials by simply incorporating their specific parameters when generating artificial images.

Dataset Information

Toward the use of CVD-grown MoS₂ nanosheets as field-emission source.

Publications

Toward the use of CVD-grown MoS<sub>2</sub> nanosheets as field-emission source.

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