Indirect adaptive 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 which 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 backpropagation neural networks. These components facilitate nonlinear system identification and control, respectively. The neural network controller is applied to controlling the etch rate and DC bias while processing a GaAs/AlGaAs metal-semiconductor-metal 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 and DC bias to that of the target values in less than 5 s. These results are shown to be superior to those of a more conventional LQG/LTR control scheme based on a linearized transfer function model of the RIE system