Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Chandrasekaran, NiranjanSubject: search.filters.subject.semiconductor manufacturing

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    Operational Models for Evaluating the Impact of Process Changes on Cluster Tool Performance
    (1999) Chandrasekaran, Niranjan; Herrmann, Jeffery; ISR
    This thesis describes operational models that integrate process models to expedite process change decisions for cluster tool performance improvement. The process engineer attempting a process change needs to wait for the industrial engineer to approve the change after making sure it does not degrade cluster tool performance. Having a model that integrates process parameters into the operational model of the tool helps the process engineer quantify the impact of process changes on tool performance.This makes the process change decision faster. Two integrated models for understanding cluster tool behavior have been developed here. One is a network model that evaluates the total time needed to process a lot of wafers for a given sequence of activities involved in the process. Including a manufacturing process model (in the form of a Response Surface Model) gives an integrated network model that relates the total lot processing time to process parameters like temperature and pressure and to process times.

    The second model developed is an integrated simulation model that can be used when the sequence of wafer moves is not given but is determined by a scheduling rule. The model can be used to quantify the impact of changes to process parameters and product characteristics like deposition thickness on total lot processing time. The thesis contains examples that illustrate the types of insights that one can gain into cluster tool behavior from using these integrated models.

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    Evaluating the Impact of Process Changes on Cluster Tool Performance
    (1999) Herrmann, Jeffrey W.; Chandrasekaran, Niranjan; Conaghan, Brian F.; Nguyen, Manh-Quan; Rubloff, Gary W.; Shi, Rock Z.; ISR
    Cluster 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.

    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.

    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.

    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).

    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.