2020年3月
Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials
NPJ 2D MATERIALS AND APPLICATIONS
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- 巻
- 4
- 号
- 1
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1038/s41699-020-0137-z
- 出版者・発行元
- NATURE RESEARCH
Deep-learning algorithms enable precise image recognition based on high-dimensional hierarchical image features. Here, we report the development and implementation of a deep-learning-based image segmentation algorithm in an autonomous robotic system to search for two-dimensional (2D) materials. We trained the neural network based on Mask-RCNN on annotated optical microscope images of 2D materials (graphene, hBN, MoS2, and WTe2). The inference algorithm is run on a 1024 x 1024 px(2) optical microscope images for 200 ms, enabling the real-time detection of 2D materials. The detection process is robust against changes in the microscopy conditions, such as illumination and color balance, which obviates the parameter-tuning process required for conventional rule-based detection algorithms. Integrating the algorithm with a motorized optical microscope enables the automated searching and cataloging of 2D materials. This development will allow researchers to utilize a large number of 2D materials simply by exfoliating and running the automated searching process. To facilitate research, we make the training codes, dataset, and model weights publicly available.
- リンク情報
- ID情報
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- DOI : 10.1038/s41699-020-0137-z
- eISSN : 2397-7132
- Web of Science ID : WOS:000521273400001