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The development of the cost of ownership methodology provided the semiconductor industry with a process that is employed to evaluate the life cycle costs of any particular equipment. Applying this technique has provided a cost focus on areas of potential improvement. The existing methodology is equipment centric. The limitation of this process is that there has not been a means of evaluating the impact of the cost of ownership for a process. An evaluation of process requirements indicated that such a tool would provide an advantage for evaluating not only the process flow cost but also allocate the individual cost of ownership values according to the planned volumes and yields. This would not be the comprehensive evaluation that can be done with dynamic simulation, but a static first approximation at total process costs based on a combined process flow. This paper describes the application of this new process to the development of the process cost of ownership to the optical mask production process. The program employed in work, PRO COOLTM, was developed by WWK in conjunction with SEMATECH. This paper describes the application of process cost of ownership to the optical mask production process sequence. Using a generic mask fabrication flow, process sequence cost of ownership analysis is used to identify cost drivers, throughput limitations, and process cost sensitivities. This generic process flow consists of the data evaluation and general number crunching requirements at the beginning of the process, followed by exposure, develop, inspection, measure, CD, pelliclize, inspect, and ship. Understanding of the relationship of these factors will help evaluate future mask fabrication technologies and requirements. Analyzing a generic optical mask production process sequence showed that the simple approach of adding process step cost of ownership values underestimates the process cost of ownership. Thus a complete analysis must consider the cost of unused capacity in the process sequence. The cost of unused capacity is correlated to the production throughput rate of the bottleneck tool. Capacity analysis helps to identify the bottleneck tool under static conditions, however, process and reliability variation can create short-term bottlenecks which must also be considered.
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