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

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. The optimized system successfully achieved significant thermal uniformity across the susceptor surface, such that the temperature fluctuations over the entire disk radius were kept within ±7% of the target temperature (1100 °C). This significant improvement confirms the effectiveness of the deep learning-based cascade approach for solving high-dimensional Multiphysics inverse problems.