Determination of the interface between amorphous insulator and crystalline 4H–SiC in transmission electron microscope image by using convolutional neural network

• Main Authors: Hironori Yoshioka, Tomonori Honda
• Format: Article
• Language: English
• Published: AIP Publishing LLC 2021-01-01
• Series: AIP Advances
• Online Access: http://dx.doi.org/10.1063/5.0036982

A rough interface seems to be one of the possible reasons for low channel mobility (conductivity) in SiC metal-oxide-semiconductor field-effect transistors. To evaluate the mobility by interface roughness, we drew a boundary line between an amorphous insulator and crystalline 4H–SiC in a cross-sectional image obtained by using a transmission electron microscope by using the deep learning approach of a convolutional neural network (CNN). We show that the CNN model recognizes the interface very well, even when the interface is too rough to draw the boundary line manually. The power spectral density of interface roughness was calculated and was comparable with those of Si interfaces, indicating that interface roughness cannot account for the low channel mobility of SiC interfaces.