Using static capacity modeling and queuing theory equations to predict factory cycle time performance in semiconductor manufacturing

In order to maximize asset utilization and meet customer delivery requirements manufacturing facilities are driven by two key metrics: utilization of production capacity and cycle time. The science of factory physics indicates that queuing theory algorithms relying on an understanding of factory variability at the equipment level can make it possible to use static calculations to estimate factory cycle times. This approach has been frequently dismissed as insufficiently accurate due to the difficulty associated with determining the required variability factors. This paper outlines a method using queuing theory equations together with targeted historical data to estimate total cycle times. Initial validation results indicate that the approach can provide sufficiently accurate results to be useful in manufacturing decision making. Equations, data requirements, and validation results are presented. Opportunities for improvement of the methodology as well as further refinement of the equations for calculating equipment specific variability factors are also discussed.