Dataset Information

Origins of Moire Patterns in CVD-grown MoS₂ Bilayer Structures at the Atomic Scales.

ABSTRACT: The chemical vapor deposition (CVD)-grown two-dimensional molybdenum disulfide (MoS₂) structures comprise of flakes of few layers with different dimensions. The top layers are relatively smaller in size than the bottom layers, resulting in the formation of edges/steps across adjacent layers. The strain response of such few-layer terraced structures is therefore likely to be different from exfoliated few-layered structures with similar dimensions without any terraces. In this study, the strain response of CVD-grown few-layered MoS₂ terraced structures is investigated at the atomic scales using classic molecular dynamics (MD) simulations. MD simulations suggest that the strain relaxation of CVD-grown triangular terraced structures is observed in the vertical displacement of the atoms across the layers that results in the formation of Moiré patterns. The Moiré islands are observed to nucleate at the corners or edges of the few-layered structure and propagate inwards under both tensile and compressive strains. The nucleation of these islands is observed to happen at tensile strains of ~ 2% and at compressive strains of ~2.5%. The vertical displacements of the atoms and the dimensions of the Moiré islands predicted using the MD simulation are in excellent agreement with that observed experimentally.

SUBMITTER: Wang J

PROVIDER: S-EPMC6013503 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Origins of Moiré Patterns in CVD-grown MoS<sub>2</sub> Bilayer Structures at the Atomic Scales.

Wang Jin J Namburu Raju R Dubey Madan M Dongare Avinash M AM

Scientific reports 20180621 1

The chemical vapor deposition (CVD)-grown two-dimensional molybdenum disulfide (MoS<sub>2</sub>) structures comprise of flakes of few layers with different dimensions. The top layers are relatively smaller in size than the bottom layers, resulting in the formation of edges/steps across adjacent layers. The strain response of such few-layer terraced structures is therefore likely to be different from exfoliated few-layered structures with similar dimensions without any terraces. In this study, th ...[more]

PMID: 29930303

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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

Origins of Moire Patterns in CVD-grown MoS₂ Bilayer Structures at the Atomic Scales.

Publications

Origins of Moiré Patterns in CVD-grown MoS<sub>2</sub> Bilayer Structures at the Atomic Scales.

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Origins of Moire Patterns in CVD-grown MoS2 Bilayer Structures at the Atomic Scales.

Publications

Origins of Moiré Patterns in CVD-grown MoS<sub>2</sub> Bilayer Structures at the Atomic Scales.

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OmicsDI is part of the ELIXIR infrastructure

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Origins of Moire Patterns in CVD-grown MoS₂ Bilayer Structures at the Atomic Scales.