Recognition of dislocation structure of silicon carbide epitaxial layers by а neural network

Technological features of the growth of single crystal silicon carbide inevitably create conditions for the formation of crystal structure defects in them. A method is proposed for recognizing and analyzing a dislocation structure of single crystal silicon carbide based on the use of optical microscopy and a direct distribution neural network. The method was tested on homoepitaxial layers of 4H-polytype silicon carbide. Software has been developed that allows building maps of the dislocation structure distribution over the surface of single crystal silicon carbide. The software was tested on digital images of the surface of silicon carbide epitaxial layers. The accuracy of recognition of dislocation structure was 95%. The dislocation mapping is used in the development of process technologies for reducing their density during the growth of single crystals.