Design of a self-tuning adaptive model predictive controller using recursive model parameter estimation for real-time plasma variable control

Abstract The semiconductor etching process, which is the most important part of the semiconductor manufacturing process, requires higher sophistication as 10 nm semiconductors are mass produced. Etching methods utilizing plasma are getting increasingly popular with the miniaturization of the etching process. As the process performance depends on the state of the plasma variables, such as electron density, it is essential to measure and control these variables in real time. Moreover, to control the plasma-based system, the sensitive and time-varying characteristics of plasma should be considered. This paper proposes a self-tuning adaptive model predictive controller that can effectively perform electron density control. As a first step, an integral squared error-based Bayesian optimization is used to tune the model predictive controller, and its performance is verified on a drift-free Ar plasma system. The self-tuning adaptive model predictive controller is constructed by combining a recursive model parameter estimator with a model predictive controller. The recursive model parameter estimator is designed using a recursive least squares algorithm with Kalman filter interpretation. The effectiveness of the proposed controller is verified through control simulations and a set-point tracking experiment on the electron density with artificial drift in real time. The experimental results show that the performance of the proposed controller is 21% better than that of the conventional model predictive controller. We expect that this result will make a significant contribution to control processes utilizing plasma.