The CNN Deep Learning-Based Melting Process Prediction of Czochralski Monocrystalline Silicon

• Published in: IEEE access 2022, Vol.10, p.41986-41992
• Main Authors: Zhang, Jing, Liu, Ding, Tang, Qin-Wei
• Format: Article
• Language: English
• Subjects: Artificial neural networks; CNN deep learning; Convolution; Convolutional neural networks; Czochralski monocrystalline silicon; Deep learning; Heating systems; Image classification; Machine learning; Melting; melting process; Polysilicon; prediction of the melting process; Silicon; Surface treatment
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3168021

To solve seeding failures due to the misjudgment caused by manual observation in the traditional melting process of Czochralski (CZ) monocrystalline silicon, a method for predicting the melting progress of CZ monocrystalline silicon based on Convolutional Neural Network (CNN) deep learning was proposed. The deep learning method and image classification of the melting process were combined. By taking CNN as the research object, the AlexNet network-based melting classification model was constructed. Meanwhile, the comparative analysis was performed by adjusting the number of AlexNet network convolution layers and the size of the convolution kernel. After several experiments, a CNN-based melting stage classification model was finally determined. Simulation results showed that the model could achieve higher accuracy when predicting the melting process. This paper focuses on the key technical issues such as polycrystalline silicon melting and temperature predication in the growth process of the monocrystalline silicon, and predicts the melting process of silicon materials, which lays the foundation for the quality improvement of monocrystalline silicon growth process in the semiconductor field.