Non‐destructive and deep learning‐enhanced characterization of 4H‐SiC material

Abstract

Abstract The silicon carbide (SiC) crystal growth is a multiple‐phase aggregation process of Si and C atoms. With the development of the clean energy industry, the 4H‐SiC has gained increasing attention as it is an ideal material for new energy automobiles and optoelectronic devices. The aggregation process is normally complex and dynamic due to its distinctive formation energy, and it is hard to study and trace back in a non‐destructive and comprehensive way. Here, this work developed a non‐destructive and deep learning‐enhanced characterization method of 4H‐SiC material, which was based on micro‐CT scanning, the verification of various optical measurements, and the convolutional neural network (ResNet‐50 architecture). Harmful defects at the micro‐level, polytypes, micropipes, and carbon inclusions could be identified and orientated with more than 96% high performance on both accuracy and precision. The three‐dimensional visual reconstruction with quantitative analyses provided a vivid tracing back of the SiC aggregation process. This work demonstrated a useful tool to understand and optimize the SiC growth technology and further enhance productivity.

Keywords

• convolutional neural network
• crystal growth
• defects
• dynamic evolution
• optical characterization
• surface morphology