Variation monitoring, diagnosis and control for complex solar cell manufacturing processes
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PublisherThe University of Arizona.
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AbstractInterest in photovoltaic products has expanded dramatically, but wide-scale commercial use remains limited due to the high manufacturing cost and insufficient efficiency of solar products. Therefore, it is critical to develop effective process monitoring, diagnosing, and control methods for quality and productivity improvement. This dissertation is motivated by this timely need to develop effective process control methods for variation reduction in thin film solar cell manufacturing processes. Three fundamental research issues related to process monitoring, diagnosis, and control have been studied accordingly. The major research activities and the corresponding contributions are summarized as follows: (1) Online SPC is integrated with generalized predictive control (GPC) for the first time for effective process monitoring and control. This research emphasizes on the importance of developing supervisory strategies, in which the controller parameters are adaptively changed based on the detection of different process change patterns using SPC techniques. It has been shown that the integration of SPC and GPC provides great potential for the development of effective controllers especially for a complex manufacturing process with a large time varying delay and different process change patterns. (2) A generic hierarchical ANOVA method is developed for systematic variation decomposition and diagnosis in batch manufacturing processes. Different from SPC, which focuses on variation reduction due to assignable causes, this research aims to reduce inherent normal process variation by assessing and diagnosing inherent variance components from production data. A systematic method of how to use a full factor decomposition model to systematically determine an appropriate nested model structure is investigated for the first time in this dissertation. (3) A Multiscale statistical process monitoring method is proposed for the first time to simultaneously detect mean shift and variance change for autocorrelated data. Three wavelet-based monitoring charts are developed to separately detect process variance change, measurement error variance change, and process mean shift simultaneously. Although the solar cell manufacturing process is used as an example in the dissertation, the developed methodologies are generic for process monitoring, diagnosis, and control in process variation reduction, which are expected to be applicable to various other semiconductor and chemical manufacturing processes.
Degree ProgramGraduate College
Systems and Industrial Engineering