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Deep-learning-based semantic image segmentation of graphene field-effect transistors
Applied Physics Express ( IF 2.3 ) Pub Date : 2021-02-19 , DOI: 10.35848/1882-0786/abe3db
Shota Ushiba 1 , Naruto Miyakawa 1 , Naoya Ito 1 , Ayumi Shinagawa 1 , Tomomi Nakano 1 , Tsuyoshi Okino 1 , Hiroki K. Sato 1 , Yuka Oka 1 , Madoka Nishio 1 , Takao Ono 2, 3 , Yasushi Kanai 2 , Seiji Innami 1 , Shinsuke Tani 1 , Masahiko Kimuara 1 , Kazuhiko Matstumoto 2
Affiliation  

Large-scale graphene films are available, which enables the integration of graphene field-effect transistor (G-FET) arrays on chips. However, the transfer characteristics are not identical but diverse over the array. Optical microscopy is widely used to inspect G-FETs, but quantitative evaluation of the optical images is challenging as they are not classified. Here, we implemented a deep-learning-based semantic image segmentation algorithm. Through a neural network, every pixel was assigned to graphene, electrode, substrate, or contaminants, with exceeding a success rate of 80%. We also found that the drain current and transconductance correlated with the coverage of graphene films.



中文翻译:

石墨烯场效应晶体管的基于深度学习的语义图像分割

可以使用大规模的石墨烯薄膜,从而可以在芯片上集成石墨烯场效应晶体管(G-FET)阵列。但是,传输特性并不相同,而是在整个阵列上各不相同。光学显微镜被广泛用于检查G-FET,但是光学图像的定量评估具有挑战性,因为它们尚未分类。在这里,我们实现了一种基于深度学习的语义图像分割算法。通过神经网络,每个像素被分配给石墨烯,电极,基底或污染物,成功率超过80%。我们还发现,漏极电流和跨导与石墨烯薄膜的覆盖率相关。

更新日期:2021-02-19
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