Deep-learning-based semantic image segmentation of graphene field-effect transistors
- 16 February 2021
- journal article
- research article
- Published by IOP Publishing in Applied Physics Express
- Vol. 14 (3), 036504
- https://doi.org/10.35848/1882-0786/abe3db
Abstract
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.Keywords
Funding Information
- Japan Science and Technology Agency (JPMJCR15F4)
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