Real-time control of reactive ion etching using neural networks

This paper explores the use of neural networks for real-time, model-based feedback control of reactive ion etching (RIE). This objective is accomplished in part by constructing a predictive model for the system that can be approximately inverted to achieve the desired control. An indirect adaptive control (IAC) strategy is pursued. The IAC structure includes a controller and plant emulator, which are implemented as two separate back-propagation neural networks. These components facilitate nonlinear system identification and control, respectively. The neural network controller is applied to controlling the etch rate of a GaAs/AlGaAs metal-semiconductor-metal (MSM) structure in a BCl₃/Cl₂ plasma using a Plasma Therm 700 SLR series RIE system. Results indicate that in the presence of disturbances and shifts in RIE performance, the IAC neural controller is able to adjust the recipe to match the etch rate to that of the target value in less than 5 s. These results are shown to be superior to those of a more conventional control scheme using the linear quadratic Gaussian method with loop-transfer recovery, which is based on a linearized transfer function model of the RIE system