Deep learning-assisted magnetized inductively coupled plasma discharge modeling

Abstract In recent years, magnetized inductively coupled plasma (MICP) has been proposed as an improved version of inductively coupled plasma to meet the increasing production process requirements. However, due to the more complex structure of the plasma system, numerical simulations face challenges such as modeling difficulty, model convergence issues, and long computation times. In this paper, a deep neural network (DNN) with a multi-hidden layer structure is developed based on deep learning technology to serve as an assisted modeling tool of traditional fluid simulations. This approach aims to study the discharge characteristics and plasma chemistry of argon-oxygen MICP more efficiently. The simulation data from the fluid model is used to train the neural network. The well-trained DNN can efficiently and accurately predict the target plasma characteristics under new discharge parameters, such as electron density, ionization rate, and particle reaction rate. The effectiveness of the DNN is verified by comparing its predictions with fluid simulation results. Compared to the traditional fluid simulation, which takes thousands of seconds, the trained DNN can produce highly consistent prediction results only within 1 s. The computing efficiency is significantly improved. The prediction results of the DNN demonstrate that by adjusting discharge parameters such as magnetic field coil current, power, and O 2 ratio, the plasma discharge characteristics and plasma chemistry can be modulated, including charged particle density, ionization rate, chemical reaction rate, and plasma radial uniformity. Additionally, the DNN shows good generalization ability across multiple input and output scales, providing a paradigm for its application in other plasma research fields. This study indicates that the DNN is a feasible method for studying MICP discharge characteristics and offers a novel and effective computational tool for future plasma discharge research based on deep learning methods.