School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Crystal Growth Equipment and System Integration National & Local Joint Engineering Research Center, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel). 2023 Jun 24;23(13):5855. doi: 10.3390/s23135855.
Monocrystalline silicon is an important raw material in the semiconductor and photovoltaic industries. In the Czochralski (CZ) method of growing monocrystalline silicon, various factors may cause node loss and lead to the failure of crystal growth. Currently, there is no efficient method to detect the node loss of monocrystalline silicon at industrial sites. Therefore, this paper proposed a monocrystalline silicon node-loss detection method based on multimodal data fusion. The aim was to explore a new data-driven approach for the study of monocrystalline silicon growth. This article first collected the diameter, temperature, and pulling speed signals as well as two-dimensional images of the meniscus. Later, the continuous wavelet transform was used to preprocess the one-dimensional signals. Finally, convolutional neural networks and attention mechanisms were used to analyze and recognize the features of multimodal data. In the article, a convolutional neural network based on an improved channel attention mechanism (ICAM-CNN) for one-dimensional signal fusion as well as a multimodal fusion network (MMFN) for multimodal data fusion was proposed, which could automatically detect node loss in the CZ silicon single-crystal growth process. The experimental results showed that the proposed methods effectively detected node-loss defects in the growth process of monocrystalline silicon with high accuracy, robustness, and real-time performance. The methods could provide effective technical support to improve efficiency and quality control in the CZ silicon single-crystal growth process.
单晶硅是半导体和光伏产业的重要原材料。在直拉法（CZ）生长单晶硅的过程中，各种因素可能导致节点损失，从而导致晶体生长失败。目前，在工业现场还没有有效的方法来检测单晶硅的节点损失。因此，本文提出了一种基于多模态数据融合的单晶硅节点损失检测方法，旨在探索一种新的数据驱动方法来研究单晶硅的生长。本文首先采集了直径、温度、拉速信号以及弯月面的二维图像。然后，使用连续小波变换对一维信号进行预处理。最后，使用卷积神经网络和注意力机制分析和识别多模态数据的特征。本文提出了一种基于改进通道注意力机制（ICAM-CNN）的一维信号融合卷积神经网络（ICAM-CNN）和多模态融合网络（MMFN），可以自动检测 CZ 硅单晶生长过程中的节点损失。实验结果表明，所提出的方法能够以高精度、鲁棒性和实时性有效地检测单晶硅生长过程中的节点损失缺陷。这些方法可以为提高 CZ 硅单晶生长过程的效率和质量控制提供有效的技术支持。
