论文标题
通过使用卷积神经网络确定透射电子显微镜图像中无定形绝缘子和晶体4H-SIC之间的界面
Determination of the Interface between Amorphous Insulator and Crystalline 4H-SiC in Transmission Electron Microscope Image by using Convolutional Neural Network
论文作者
论文摘要
粗糙的界面似乎是SIC MOSFET中低通道迁移率（电导率）的可能原因之一。为了通过界面粗糙度评估迁移率，我们使用卷积神经网络（CNN）的深度学习方法在通过透射电子显微镜（TEM）获得的横截面图像中绘制了无定形绝缘体和结晶4H-SIC之间的边界线。我们表明，即使接口太粗糙而无法手动绘制边界线，CNN模型也很好地识别了接口。计算界面粗糙度的功率谱密度。
A rough interface seems to be one of the possible reasons for low channel mobility (conductivity) in SiC MOSFETs. To evaluate the mobility by interface roughness, we drew a boundary line between amorphous insulator and crystalline 4H-SiC in a cross-sectional image obtained by a transmission electron microscope (TEM), by using the deep learning approach of 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. Power spectral density of interface roughness was calculated.
