Maintaining population diversity in a genetic algorithm: an example in developing control schemes for semiconductor manufacturing
Edward A. Rietman · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
Genetic algorithms are a computational paradigm modeled after biological genetics. They allow one to efficiently search a very large optimization-space for good solutions. In this paper we report on two methods of maintaining genetic diversity in a population of organisms being acted on by a genetic algorithm. In both cases the organisms are on a square grid and only interact with their nearest neighbors. The number of interactions is based on the fitness. One method results in ecological niches in sizes from a few organisms to several dozen. In the second method almost every organism in the population remains in a unique ecological niche searching the fitness landscape. The two methods can be used in finding multiple solutions. These methods have been applied to a semiconductor manufacturing process in developing robust plasma etch recipes that reduce the variance about a target mean and allow the dc bias to drift within 15% of a nominal value. The tapered via etch process in our production environment results in an oxide film with a mean value of about 7093 angstroms and a standard deviation of 730 angstroms. In simulations using real production data and a neural network model of the process our new recipes have reduced the standard deviation below 200 angstroms. These results indicate that significant improvement in the proces can be realized by applying these techniques.