Real-time 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 that ran be ap proximately inverted to achieve the desired control. An indirect adaptive c ontrol (IAC) strategy is pursued. The IAC structure includes a controller a nd plant emulator, which are implemented as two separate back-propagation n eural networks. These components facilitate nonlinear system identification and control, respectively. The neural network controller is applied to con trolling the etch rate of a GaAs/AlGaAs metal-semiconductor-metal (MSM) str ucture in a BCl3/Cl-2 plasma using a Plasma Therm 700 SLR series RIE system , Results indicate that in the presence of disturbances and shifts in RIE p erformance, 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 usi ng the linear quadratic Gaussian method with loop-transfer recovery, which is based on a linearized transfer function model of the RIE system.