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 replace 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 experimental diagnostics and fluid simulation results. Compared to the traditional fluid simulation, which takes thousands of seconds, the DNN only requires hundreds of seconds to produce highly consistent prediction results, thereby improving computational efficiency by approximately nine times. The prediction results of the