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.Rubloff, Gary W.Subject: search.filters.subject.semiconductor manufacturing

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    Influence of Gas Composition on Wafer Temperature Control in a Tungsten Chemical Vapor Deposition Reactor
    (2000) Chang, Hsiao-Yung; Adomaitis, Raymond A.; Kidder, John N., Jr.; Rubloff, Gary W.; ISR
    Experimental measurements of wafer temperature in a single-wafer,lamp-heated CVD system were used to study the wafer temperature responseto gas composition. A physically based simulation procedure for theprocess gas and wafer temperature was developed in which a subset ofparameter values were estimated using a nonlinear, iterative parameteridentification method, producing a validated model with true predictivecapabilities.

    With process heating lamp power held constant, wafertemperature variations of up to 160 degrees K were observed by varying feed gasH_2/N_2 ratio. Heat transfer between the wafer and susceptor wasstudied by shifting the instrumented wafer off the susceptor axis,exposing a portion of the wafer backside to the chamber floor. Modelpredictions and experimental observations both demonstrated that the gasvelocity field had little influence on the observed wafer and predictedgas temperatures.

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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.