Biomedical Image Edge Detection Based on CNN

Tang, Shusen; Fei, Keling; Wang, Wei

doi:10.1145/3180496.3180611

Proceedings of the 2017 International Conference on Wireless Communications, Networking and Applications 2017

DOI: 10.1145/3180496.3180611

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Biomedical Image Edge Detection Based on CNN

Shusen Tang

Keling Fei

Wei Wang

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2024

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Using U-Net convolutional neural network to model pixel-based electrostatic potential distributions in GaN power MIS-HEMTs

Chen,

Hsiao,

Lin

et al. 2024

Sci Rep

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This study demonstrates a novel use of the U-Net convolutional neural network (CNN) for modeling pixel-based electrostatic potential distributions in GaN metal–insulator-semiconductor high-electron mobility transistors (MIS-HEMTs) with various gate and source field plate designs and drain voltages. The pixel-based images of the potential distribution are successfully modeled from the developed U-Net CNN with an error of less than 1% error relative to a TCAD simulated reference of a 500-V electrostatic potential distribution in the AlGaN/GaN interface. Furthermore, the modeling time of potential distributions by U-Net takes about 80 ms. Therefore, the U-Net CNN is a promising approach to efficiently model the pixel-based distributions characteristics in GaN power devices.

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Using U-Net convolutional neural network to model pixel-based electrostatic potential distributions in GaN power MIS-HEMTs

Chen,

Hsiao,

Lin

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

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Biomedical Image Edge Detection Based on CNN

Cited by 1 publication

References 7 publications

Using U-Net convolutional neural network to model pixel-based electrostatic potential distributions in GaN power MIS-HEMTs

Using U-Net convolutional neural network to model pixel-based electrostatic potential distributions in GaN power MIS-HEMTs

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