MaxFlow theory for availability calculation of automated material handling systems
Semiconductor manufacturing requires complex and expensive equipment and systems. Therefore, high utilization of the tools is important, and such quick and automated transport of wafers between the various process steps becomes the key to effective mass production. AMD's Fab 30 has therefore an automated material handling system (AMHS). The performance of such a system has immediate influence on the effectiveness of the production. That makes it necessary to discover the weaknesses of the system before they show up. The key value to describe the performance of a system is the availability. Usually, the availability is measured during a long period of time and can be determined upfront, e.g. by simulation. Here, the availability is monitored during long-term simulation runs and sums up to realistic results, but the procedure requires large modeling and calculation efforts. The availability can also be understood as performance of the system at one specific moment. The easiest way to calculate such time independent availability is to compute the mean of the availabilities of all components of the system. This is simple, but does not deliver reasonable results, because of redundancies and/or shared resources. Consequently, there is a need for quick but reasonable estimates of remaining availabilities. This paper presents the MaxFlow approach applied to semiconductor manufacturing. It allows retrieval of acceptable results with legitimate calculation effort. The MaxFlow approach was applied to the AMHS of Fab 30, in order to support daily operational decisions.
automated material handling systems semiconductor manufacturing maxflow approach