Using U-Net convolutional neural network to model pixel-based electrostatic potential distributions in GaN power MIS-HEMTs.
Sci Rep
; 14(1): 8151, 2024 Apr 08.
Article
em En
| MEDLINE
| ID: mdl-38589538
ABSTRACT
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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MEDLINE
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En
Ano de publicação:
2024
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Article