Identification and classification of exfoliated graphene flakes from microscopy images using a hierarchical deep convolutional neural network Article Swipe
Soroush Mahjoubi
,
Fan Ye
,
Yi Bao
,
Weina Meng
,
Xian Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.15252
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.15252
Identification of the mechanically exfoliated graphene flakes and classification of the thickness is important in the nanomanufacturing of next-generation materials and devices that overcome the bottleneck of Moore's Law. Currently, identification and classification of exfoliated graphene flakes are conducted by human via inspecting the optical microscope images. The existing state-of-the-art automatic identification by machine learning is not able to accommodate images with different backgrounds while different backgrounds are unavoidable in experiments. This paper presents a deep learning method to automatically identify and classify the thickness of exfoliated graphene flakes on Si/SiO2 substrates from optical microscope images with various settings and background colors. The presented method uses a hierarchical deep convolutional neural network that is capable of learning new images while preserving the knowledge from previous images. The deep learning model was trained and used to classify exfoliated graphene flakes into monolayer (1L), bi-layer (2L), tri-layer (3L), four-to-six-layer (4-6L), seven-to-ten-layer (7-10L), and bulk categories. Compared with existing machine learning methods, the presented method possesses high accuracy and efficiency as well as robustness to the backgrounds and resolutions of images. The results indicated that our deep learning model has accuracy as high as 99% in identifying and classifying exfoliated graphene flakes. This research will shed light on scaled-up manufacturing and characterization of graphene for advanced materials and devices.
Related Topics
Concepts
Artificial intelligence
Convolutional neural network
Graphene
Deep learning
Computer science
Materials science
Identification (biology)
Nanomanufacturing
Bottleneck
Artificial neural network
Robustness (evolution)
Machine learning
Layer (electronics)
Pattern recognition (psychology)
Microscopy
Nanotechnology
Computer vision
Optics
Physics
Chemistry
Embedded system
Biology
Biochemistry
Gene
Botany
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2203.15252
- https://arxiv.org/pdf/2203.15252
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221151187
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221151187Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2203.15252Digital Object Identifier
- Title
-
Identification and classification of exfoliated graphene flakes from microscopy images using a hierarchical deep convolutional neural networkWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-29Full publication date if available
- Authors
-
Soroush Mahjoubi, Fan Ye, Yi Bao, Weina Meng, Xian ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2203.15252Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2203.15252Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2203.15252Direct OA link when available
- Concepts
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Artificial intelligence, Convolutional neural network, Graphene, Deep learning, Computer science, Materials science, Identification (biology), Nanomanufacturing, Bottleneck, Artificial neural network, Robustness (evolution), Machine learning, Layer (electronics), Pattern recognition (psychology), Microscopy, Nanotechnology, Computer vision, Optics, Physics, Chemistry, Embedded system, Biology, Biochemistry, Gene, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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