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

Title: A Cascade Deep Learning Approach for Design and Control Optimization of a Dual-Frequency Induction Heating Device
Authors: Ghafoorinejad A.; Di Barba P.; Dughiero F.; Forzan M.; Mognaschi M. E.; Sieni E.
Contributors: Ghafoorinejad, A.; Di Barba, P.; Dughiero, F.; Forzan, M.; Mognaschi, M. E.; Sieni, E.
Publication Year: 2025
Collection: IRInSubria - Institutional Repository Insubria (Università degli Studi dell’Insubria)
Subject Terms: inverse problem; multi-physics domain; induction heating; finite element analysi; deep neural networks
Description: 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 degrees C) improves layer quality, increases efficiency, and reduces operating costs of epitaxy reactors.
Document Type: article in journal/newspaper
File Description: ELETTRONICO
Language: English
Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:001646451400001; volume:18; issue:24; firstpage:1; lastpage:20; numberofpages:20; journal:ENERGIES; https://hdl.handle.net/11383/2203672
DOI: 10.3390/en18246598
Availability: https://hdl.handle.net/11383/2203672; https://doi.org/10.3390/en18246598
Rights: info:eu-repo/semantics/openAccess ; license:Creative commons ; license uri:http://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.F7140374
Database: BASE