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A Cascade Deep Learning Approach for Design and Control Optimization of a Dual-Frequency Induction Heating Device.

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Title: A Cascade Deep Learning Approach for Design and Control Optimization of a Dual-Frequency Induction Heating Device.
Authors: Ghafoorinejad, Arash1 (AUTHOR), Di Barba, Paolo1,2 (AUTHOR), Dughiero, Fabrizio2,3 (AUTHOR), Forzan, Michele1,2 (AUTHOR), Mognaschi, Maria Evelina1,2 (AUTHOR) eve.mognaschi@unipv.it, Sieni, Elisabetta3 (AUTHOR)
Source: Energies (19961073). Dec2025, Vol. 18 Issue 24, p6598. 20p.
Subjects: Induction heating, Semiconductor manufacturing, Finite element method, Artificial neural networks, Thermal stability, Deep learning, Temperature control, Epitaxy
Abstract: A cascade deep learning approach is proposed for optimizing the design and control of a dual-frequency induction heating system used in semiconductor manufacturing. The system is composed of two independent power inductors, fed at different frequencies, to achieve a homogeneous temperature profile along a graphite susceptor surface, crucial for enhancing layer quality and integrity. The optimization process considers both electrical (current magnitudes and frequencies) and geometrical parameters of the coils, which influence the power penetration and subsequent temperature distribution within the graphite disk. A two-step procedure based on deep neural networks (DNNs) is employed. The first step, namely optimal design, identifies the optimal operating frequencies and geometrical parameters of the two coils. The second step, namely optimal control, determines the optimal current magnitudes. The DNNs are trained using a database generated through finite element (FE) analysis. This deep learning-based cascade approach reduces computational time and multiphysics simulations compared to classical methods by reducing the dimensionality of parameter mapping. Therefore, the proposed method proves to be effective in solving high-dimensional multiphysics inverse problems. From the application point of view, achieving thermal uniformity (±7% fluctuation at 1100 °C) improves layer quality, increases efficiency, and reduces operating costs of epitaxy reactors. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Cascade Deep Learning Approach for Design and Control Optimization of a Dual-Frequency Induction Heating Device.
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  Data: <searchLink fieldCode="DE" term="%22Induction+heating%22">Induction heating</searchLink><br /><searchLink fieldCode="DE" term="%22Semiconductor+manufacturing%22">Semiconductor manufacturing</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Thermal+stability%22">Thermal stability</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Temperature+control%22">Temperature control</searchLink><br /><searchLink fieldCode="DE" term="%22Epitaxy%22">Epitaxy</searchLink>
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  Data: A cascade deep learning approach is proposed for optimizing the design and control of a dual-frequency induction heating system used in semiconductor manufacturing. The system is composed of two independent power inductors, fed at different frequencies, to achieve a homogeneous temperature profile along a graphite susceptor surface, crucial for enhancing layer quality and integrity. The optimization process considers both electrical (current magnitudes and frequencies) and geometrical parameters of the coils, which influence the power penetration and subsequent temperature distribution within the graphite disk. A two-step procedure based on deep neural networks (DNNs) is employed. The first step, namely optimal design, identifies the optimal operating frequencies and geometrical parameters of the two coils. The second step, namely optimal control, determines the optimal current magnitudes. The DNNs are trained using a database generated through finite element (FE) analysis. This deep learning-based cascade approach reduces computational time and multiphysics simulations compared to classical methods by reducing the dimensionality of parameter mapping. Therefore, the proposed method proves to be effective in solving high-dimensional multiphysics inverse problems. From the application point of view, achieving thermal uniformity (±7% fluctuation at 1100 °C) improves layer quality, increases efficiency, and reduces operating costs of epitaxy reactors. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/en18246598
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        Text: English
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        Type: general
      – SubjectFull: Semiconductor manufacturing
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      – SubjectFull: Finite element method
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Thermal stability
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      – SubjectFull: Deep learning
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      – SubjectFull: Temperature control
        Type: general
      – SubjectFull: Epitaxy
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      – TitleFull: A Cascade Deep Learning Approach for Design and Control Optimization of a Dual-Frequency Induction Heating Device.
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              Text: Dec2025
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