Semiconductor manufacturing is a capital-intensive industry. How to utilize billions of dollars of equipment as efficiently as possible is a critical factor for a semiconductor manufacturer to succeed in stiff competition. Unlike operations management techniques, like planning and scheduling, which are proven to improve tool performance by controlling WIP (work-in-process) movement, tool science techniques focus on tool architecture, components and operations inside the tool. In this paper, we first studied process time behavior of a cluster tool and fixed inefficient process sequence. A Petri Net model was then created to determine the internal bottleneck component of the tool. Results indicated that tool science techniques helped improve tool efficiency and resulted in significant cost savings.