首页|Image segmentation of exfoliated two-dimensional materials by generative adversarial network-based data augmentation

Image segmentation of exfoliated two-dimensional materials by generative adversarial network-based data augmentation

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Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90％.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.

two-dimensional materialsdeep learningdata augmentationgenerating adversarial networks

程晓昱、解晨雪、刘宇伦、白瑞雪、肖南海、任琰博、张喜林、马惠、蒋崇云

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College of Electronic Information and Optical Engineering,Nankai University,Tianjin 300350,China

School of Physical Science and Technology,Tiangong University,Tianjin 300387,China

国家重点研发计划国家自然科学基金国家自然科学基金天津市自然科学基金天津市自然科学基金天津市教委项目中央高校基本科研业务费专项Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin and the Engineering Research Center of Thin Fi

2022YFB2803900619740756170412122JCZDJC0046019JCQNJC007002019KJ02822JCZDJC00460

2024

10.1088/1674-1056/ad23d8

中国物理B(英文版)
中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI
影响因子：0.995
ISSN：1674-1056
年,卷(期)：2024.33(3)
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