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

作　　者：Xiaofang Ye Aizhong Zhang Jiaxin Huang Wenyu Kang Wei Jiang Xu Li Jun Yin Junyong Kang 

机构地区：[1]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 [2]Hefei National Laboratory,Hefei,China

出　　处：《Aggregate》2024年第3期409-420,共12页聚集体（英文）

基　　金：Fundamental Research Funds for the Central Universities,Grant/Award Number:20720220036;National Key Research and Development Program of China,Grant/Award Number:2021YFB3401604;Key Scientific and Technological Program of Xiamen,Grant/Award Number:3502Z20231014;Innovation Program for Quantum Science and Technology,Grant/Award Number:2021ZD0303400。

摘　　要：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 use-ful tool to understand and optimize the SiC growth technology and further enhance productivity.

关 键 词：convolutional neural network crystal growth DEFECTS dynamic evolution optical characterization surface morphology 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]