Yasunori Okano1,*, Tsuyoshi Miyamoto1, Sadik Dost2
The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011685
Abstract We have developed a machine learning model, called Hybrid-PINNs (Physics Informed Neural Networks), and applied for fast predictions of transport structures (flow and thermal fields) in the silicon (Si) melt during the Czochralski (Cz) bulk single crystal growth. Si bulk single crystals are mostly grown by the Cz method. For the growth of high-quality Si crystals with this method, it is essential to understand and control these transport structures in the melt. Since the direct observation of such transport fields in the melt during growth is usually impossible, numerical simulations provide a powerful tool for… More >
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