A Deep Learning Based Dislocation Detection Method for Cylindrical Crystal Growth Process

• Main Authors: Jun Zhang, Hua Liu, Jianwei Cao, Weidong Zhu, Bo Jin, Wei Li
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Applied Sciences
• Subjects: crystal furnace; cylindrical growth; habit line; deep learning; convolutional neural networks; transfer learning
• Online Access: https://www.mdpi.com/2076-3417/10/21/7799

For the single crystal furnace used in the photovoltaic industry, growth problems occur frequently due to dislocations during the shouldering and cylindrical growth steps of the Czochralski (CZ) crystal growth. Detecting the dislocation phenomenon in the cylindrical growth step is very important for entire automation of the CZ crystal furnace, since this process usually lasts for more than 48h. The irregular nature of different patterns of dislocation would impose a big challenge for a traditional machine vision-based detection method. As almost no publications have been dedicated to detecting this phenomenon, to address this issue, after analyzing the characteristics of the silicon ingot image of this process, this paper proposes a kind of deep learning-based dislocation detection method along with tracking strategy to simulate manual inspection. The model has a good detection effect whether there is occlusion or not, the experimental results show that the detection accuracy is 97.33%, and the inference speed is about 14.7 frames per second (FPS). It can achieve the purpose of reducing energy consumption and improving process automation by monitoring this process.