Multiscale modelling and control of inductively coupled plasma etching based on recurrent neural networks
by Lihong Zhu; Qingchuan Chen; Junwei Nie; Kewei Liu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 28, No. 1, 2025

Abstract: In order to deepen the understanding of the process flow and improve the process, the plasma etching simulation is an essential means, but the calculation cost of the traditional simulation system is too high. Therefore, this study proposes a multi-scale model optimisation algorithm and model prediction and control system based on recurrent neural network. The multi-scale model optimisation algorithm adopts a recursive neural network model to achieve simulation acceleration of the multi-scale model. The model prediction science system uses model reduction and recurrent neural network models to achieve full state prediction of macroscopic plasma models. The experimental results indicate that the simulation model can achieve the expected simulation results within 7.3 seconds. Moreover, the average etching depth of the model predictive control system shall not exceed 4.6%. Consequently, the proposed simulation model optimisation algorithm and model prediction and control system can effectively optimise and control the plasma etching.

Online publication date: Mon, 02-Dec-2024

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