Production data based optimal etch time control design for a reactive ion etching process

This paper addresses the issue of end point detection and etch time control for a reactive ion etch process. Our approach involves the use of neural networks to model the functional relationship between an end point detection signal, as well as various in situ measurements, and the resulting film thickness remaining. An optimization algorithm is then employed to determine the optimal etch time based on the neural network model in order to achieve the desired level of film thickness remaining. This circumvents the need for monitoring and operating on noisy end point detection signals typically associated with conventional detection schemes. Simulation studies based on production data are presented to further demonstrate the associated design procedures and the feasibility of the algorithm. Tested on data from 89 randomly selected wafers, our controller yields a film thickness distribution with the standard deviation of 6.42 Å, a 50% improvement over the scheme currently implemented in production