Indirect adaptive control of reactive ion etching using neural networks

This paper explores the use of neural networks for real-time, model-based f eedback control of reactive ion etching (RIE). This objective is accomplish ed in part by constructing a predictive model for the system which can be a pproximately 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 identificatio n and control, respectively. The neural network controller is applied to co ntrolling the etch rate and de bias while processing a GaAs/AlGaAs metal-se miconductor-metal (MSM) structure in a BCl3/Cl-2 plasma using a Plasma Ther m 700 SLR series RIE system. Results indicate that in the presence of distu rbances 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 re suits are shown to be superior to those of a more conventional LQG/LTR control scheme based on a linearized transfe r function model of the RIE system.