Title: Multiscale modelling and control of inductively coupled plasma etching based on recurrent neural networks

Authors: Lihong Zhu; Qingchuan Chen; Junwei Nie; Kewei Liu

Addresses: Southwestern Institute of Physics, Chengdu, Sichuan, China; Department of Automation Engineering, The Engineering and Technical College of Chengdu University of Technology, Leshan, Chengdu, Sichuan, China ' Southwestern Institute of Physics, Chengdu, Sichuan, China ' Southwestern Institute of Physics, Chengdu, Sichuan, China ' Southwestern Institute of Physics, Chengdu, Sichuan, China; Engineering Training Centre, The Engineering and Technical College of Chengdu University of Technology, Leshan, Chengdu, Sichuan, China

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.

Keywords: multi-scale model; plasma etching; recurrent neural network; model reduction.

DOI: 10.1504/IJWMC.2025.143020

International Journal of Wireless and Mobile Computing, 2025 Vol.28 No.1, pp.34 - 46

Received: 25 Aug 2023
Accepted: 01 Apr 2024
Published online: 02 Dec 2024 *

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