GOALI: Adjustment and Monitoring Methods for Multiple-Stream and Process-Oriented Quality Control

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Multivariate statistical process control research has produced tools that can be used to identify when irregularities in production occur and to characterize the components of this variation. The diagnosis and control actions, in the sense of process adjustment, are not modeled and it is up to the process engineer to interpret and correct causes of variation. The presence of quality characteristics that drift with time (auto-correlation) and that vary in similar ways across several characteristics (cross-correlation) makes multivariate statistical process control a difficult task. For these reasons, interest exists on integrating process adjustment techniques with statistical process monitoring tools. The major components of variation in quality data can be found by decomposing the data according to principal component analysis, but this is a data-oriented approach and not based on any process knowledge, which makes interpretation difficult. The process-oriented basis representation (POBREP) analysis uses process knowledge to decompose quality data into cause-associated components. In POBREP, each potential production problem is associated with one basis element. This research investigates the thesis that POBREP can provide an effective tool for process adjustment. It has been shown previously how POBREP can be used for process monitoring purposes. For process adjustment, the following questions, among others, will be investigated: (1) What are the appropriate statistical models for adjustment that incorporate POBREP knowledge? (2) When is POBREP likely to provide a performance advantage? and (3) Can POBREP be applied effectively to a wafer fabrication process? There are several benefits associated with this research. A monitoring and adjustment strategy based on anticipated problems and disturbances can transform the ineffective performance of an omnibus control strategy. The work includes collaboration between researchers at Arizona State, Intel, and Penn State. The collaboration includes eight-month internships at Intel for Penn State and Arizona State graduate research assistants, visits to Intel by Penn State faculty, and regular visits to Intel by Arizona State faculty. There are extensive infrastructure benefits related to this collaborative approach: (1) synergistic benefits of coordinated research from four previously separately sponsored NSF researchers; (2) GOALI benefits, including engineers in the classroom, faculty visits to industry, etc.; (3) the opportunity to leverage results using existing laboratory equipment; and (4) to enhance existing courses in Applied Statistics at Penn State and Arizona State.