Fast Prediction of Transport Structures in the Melt by Physics Informed Neural Networks during ‘VMCz’ Crystal Growth of Silicon

Fast prediction of fluid flow and thermal fields during the growth of bulk silicon single crystals by the ‘Vertical Magnetic Field Applied Czochralski (VMCz) Method’ was successfully achieved by the application of Physics Informed Neural Networks (PINNs) without any answer-labeled training data gene...

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Bibliographic Details
Published in:Journal of chemical engineering of Japan 2023-12, Vol.56 (1)
Main Authors: Takehara, Yuto, Okano, Yasunori, Dost, Sadik
Format: Article
Language:English
Subjects:
Computer simulation
Crystal growth
Crystals
Czochralski method
Finite element method
Fluid flow
Magnetic fields
Mathematical models
Neural networks
Numerical prediction
Physics
Physics informed neural networks
Predictions
Silicon
Silicon single crystal
Single crystals
Transport phenomena
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Summary:Fast prediction of fluid flow and thermal fields during the growth of bulk silicon single crystals by the ‘Vertical Magnetic Field Applied Czochralski (VMCz) Method’ was successfully achieved by the application of Physics Informed Neural Networks (PINNs) without any answer-labeled training data generated by a numerical simulation. The PINNs’ results are in good agreement with those of the numerical simulation. The prediction time by PINNs was significantly reduced; to less than 0.1 seconds compared with about 30 minutes required by the numerical simulation. Moreover, being mesh-free techniques, PINNs do not require mesh reconstruction to accommodate the change in the growth melt volume during growth. This shows that PINNs have great potential, as real-time simulation techniques, for future applications in various areas.
ISSN:0021-9592
1881-1299
DOI:10.1080/00219592.2023.2236656