Deep learning-based quality filtering of mechanically exfoliated 2D crystals

Saito, Yu; Shin, Kento; Terayama, Kei; Desai, Shaan; Onga, Masaru; Nakagawa, Yuji; Itahashi, Yuki M.; Iwasa, Yoshihiro; Yamada, Makoto; Tsuda, Koji

doi:10.48550/arxiv.1907.03239

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…Further forward, deep learning enables the segmentation of various 2D materials. [54][55][56][57] Through training with many flake images, the neural network develops a skill to identify atomic layers on SiO 2 /Si substrates. The algorithm based on deep learning is implemented in the automated searching system 41) described in Sect.…”

Section: Determination Of Flake Thickness By Image Analysismentioning

confidence: 99%

Assembly of van der Waals heterostructures: exfoliation, searching, and stacking of 2D materials

Onodera

Masubuchi

Moriya

et al. 2020

Jpn. J. Appl. Phys.

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The fascinating point of 2D and layered materials is that they can be assembled into van der Waals (vdW) heterostructures, in which atomic layers are integrated by vdW force. There are almost infinite potential combinations in vdW heterostructures owing to the multiple degrees of freedom, i.e., the choice of materials, stacking order, and lateral orientation angle at the interfaces. In this article, we review the fabrication technique of vdW heterostructures, which has played an essential role in the development of the 2D materials research field. First, we describe the primary technique of mechanical exfoliation to fabricate and identify high-quality atomic layers. We then discuss the assembly of atomic layers into vdW heterostructures. Finally, we introduce the recent advancement of fabrication techniques using autonomous robotic assembly. We hope this article would help the readers to acquire basic knowledge of vdW assembly and motivate them to fabricate vdW heterostructures.

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Section: Determination Of Flake Thickness By Image Analysismentioning

confidence: 99%

Assembly of van der Waals heterostructures: exfoliation, searching, and stacking of 2D materials

Onodera

Masubuchi

Moriya

et al. 2020

Jpn. J. Appl. Phys.

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show abstract

“…However, the observed difference in contrast and color of a flake with respect to the background does not only depend on its thickness and material, but also on the substrate that is used and on the settings of the microscope. This large parameter space makes the identification of usable flakes tedious and, while there exist proposed algorithmic solutions [14][15][16][17][18][19][20][21], a sufficiently general and fast algorithm is difficult to formulate.…”

Section: Introductionmentioning

confidence: 99%

Fully automated identification of 2D material samples

Greplova,

Gold,

Kratochwil

et al. 2019

Preprint

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Thin nanomaterials are key constituents of modern quantum technologies and materials research. Identifying specimens of these materials with properties required for the development of state of the art quantum devices is usually a complex and lengthy human task. In this work we provide a neural-network driven solution that allows for accurate and efficient scanning, data-processing and sample identification of experimentally relevant two-dimensional materials. We show how to approach classification of imperfect imbalanced data sets using an iterative application of multiple noisy neural networks. We embed the trained classifier into a comprehensive solution for end-to-end automatized data processing and sample identification.

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Deep learning-based quality filtering of mechanically exfoliated 2D crystals

Cited by 2 publications

References 35 publications

Assembly of van der Waals heterostructures: exfoliation, searching, and stacking of 2D materials

Assembly of van der Waals heterostructures: exfoliation, searching, and stacking of 2D materials

Fully automated identification of 2D material samples

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