Non‐destructive and deep learning‐enhanced characterization of 4H‐SiC material
Xiaofang Ye,
Aizhong Zhang,
Jiaxin Huang,
Wenyu Kang,
Wei Jiang,
Xu Li,
Jun Yin,
Junyong Kang
Affiliations
Xiaofang Ye
Engineering Research Center of Micro‐nano Optoelectronic Materials and Devices Ministry of Education College of Physical Science and Technology Tan Kah Kee Innovation Laboratory, College of Chemistry and Chemical Engineering Pen‐Tung Sah Institute of Micro‐Nano Science and Technology Xiamen University Xiamen China
Aizhong Zhang
Engineering Research Center of Micro‐nano Optoelectronic Materials and Devices Ministry of Education College of Physical Science and Technology Tan Kah Kee Innovation Laboratory, College of Chemistry and Chemical Engineering Pen‐Tung Sah Institute of Micro‐Nano Science and Technology Xiamen University Xiamen China
Jiaxin Huang
Engineering Research Center of Micro‐nano Optoelectronic Materials and Devices Ministry of Education College of Physical Science and Technology Tan Kah Kee Innovation Laboratory, College of Chemistry and Chemical Engineering Pen‐Tung Sah Institute of Micro‐Nano Science and Technology Xiamen University Xiamen China
Wenyu Kang
Engineering Research Center of Micro‐nano Optoelectronic Materials and Devices Ministry of Education College of Physical Science and Technology Tan Kah Kee Innovation Laboratory, College of Chemistry and Chemical Engineering Pen‐Tung Sah Institute of Micro‐Nano Science and Technology Xiamen University Xiamen China
Wei Jiang
Engineering Research Center of Micro‐nano Optoelectronic Materials and Devices Ministry of Education College of Physical Science and Technology Tan Kah Kee Innovation Laboratory, College of Chemistry and Chemical Engineering Pen‐Tung Sah Institute of Micro‐Nano Science and Technology Xiamen University Xiamen China
Xu Li
Engineering Research Center of Micro‐nano Optoelectronic Materials and Devices Ministry of Education College of Physical Science and Technology Tan Kah Kee Innovation Laboratory, College of Chemistry and Chemical Engineering Pen‐Tung Sah Institute of Micro‐Nano Science and Technology Xiamen University Xiamen China
Jun Yin
Engineering Research Center of Micro‐nano Optoelectronic Materials and Devices Ministry of Education College of Physical Science and Technology Tan Kah Kee Innovation Laboratory, College of Chemistry and Chemical Engineering Pen‐Tung Sah Institute of Micro‐Nano Science and Technology Xiamen University Xiamen China
Junyong Kang
Engineering Research Center of Micro‐nano Optoelectronic Materials and Devices Ministry of Education College of Physical Science and Technology Tan Kah Kee Innovation Laboratory, College of Chemistry and Chemical Engineering Pen‐Tung Sah Institute of Micro‐Nano Science and Technology Xiamen University Xiamen China
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.
