Neural optimal etch time controller for reactive ion etching

In this article we address a neural network-based end point detection scheme for reactive ion etching process. We use the etch time as the critical parameter to indicate process end point. Further, our approach involves the use of a neural network-based predictive model relating various in situ measurements and end point detection signal to the resulting film thickness remaining in combination with an optimization algorithm. This circumvents the need for monitoring and operating on noisy end point detection signal typically associated with conventional detection schemes. Finally, we present simulation studies based on production data to further demonstrate the associated design procedures and the feasibility of the algorithm.