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dc.contributor.authorHerrmann, Jeffrey W.en_US
dc.contributor.authorChandrasekaran, Niranjanen_US
dc.contributor.authorConaghan, Brian F.en_US
dc.contributor.authorNguyen, Manh-Quanen_US
dc.contributor.authorRubloff, Gary W.en_US
dc.contributor.authorShi, Rock Z.en_US
dc.date.accessioned2007-05-23T10:07:12Z
dc.date.available2007-05-23T10:07:12Z
dc.date.issued1999en_US
dc.identifier.urihttp://hdl.handle.net/1903/6020
dc.description.abstractCluster tools are highly integrated machines that can perform a sequence of semiconductor manufacturing processes. Their integrated nature can complicate analysis when evaluating how process changes affect the overall tool performance. <p>This paper presents two integrated models for understanding cluster tool behavior. The first model is a network model that evaluates the total lot processing time for a given sequence of activities. By including a manufacturing process model (in the form of a response surface model, or RSM), the model calculates the total lot processing time as a function of the process parameter values and other operation times. This model allows one to quantify the sensitivity of total lot processing time with respect to process parameters and times.<p>In addition, we present an integrated simulation model that includes a process model. For a given scheduling rule that the cluster tool uses to sequence wafer movements, one can use the simulation to evaluate the impact of process changes including changes to product characteristics and changes to process parameter values. In addition, one can construct an integrated network model to quantify the sensitivity of total lot processing time with respect to process times and process parameters in a specific scenario.<p>The examples presented here illustrate the types of insights that one can gain from using such methods. Namely, the total lot processing time is a function not simply of each operation's process time, but specifically of the chosen process parameter values. Modifying the process parameter values may have significant impacts on the manufacturing system performance, a consequence of importance which is not readily obvious to a process engineer when tuning a process (though in some cases, reducing process times may not change the total lot processing time much). <p>Additionally, since the cluster tool's maximum throughput depends upon the process parameters, the tradeoffs between process performance and throughput should be considered when evaluating potential process changes and their manufacturing impact.en_US
dc.format.extent216123 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1999-30en_US
dc.subjectsemiconductor manufacturingen_US
dc.subjectcluster toolsen_US
dc.titleEvaluating the Impact of Process Changes on Cluster Tool Performanceen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US


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