Analysis of Correlated Functional Variables for Manufacturing Process Diagnosis

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Functional process variables (FPVs) play a significant role in determining the performance of various manufacturing processes. An example of FPVs is the distribution of mechanical pressure on a wafer during chemical-mechanical polishing (CMP). The analysis of the correlation among FPVs will provide, e.g., a better understanding of the complex wafer-pad-slurry interactions in the CMP process. The improved understanding could affect 20% of wafer yield and impact a revenue stream of $2.8 billion for a single wafer fab. Therefore, the objective of the project is to develop a novel methodology for the analysis of correlated FPVs in order to achieve effective monitoring and diagnosis of complex manufacturing processes. The research will first model the complex temporal and spatial variations in correlated FPVs. For temporal variations, each FPV will be decomposed into amplitude and phase components for distinguishing the timing correlation and magnitude correlation. Global-local decomposition of FPVs will be performed to discriminate global and local variations. As to the spatial variations, a nonlinear dynamics model will be used to depict the timing correlation among FPVs. A nonlinear principal component method will be developed to model the magnitude (amplitude/global/local) correlation among FPVs. Based on the FPVs modeling, statistical procedures will be developed to detect and diagnose variations in correlated FPVs. The change of timing correlation in FPVs can be diagnosed through investigating the coefficients in the nonlinear dynamics model. The change of magnitude correlation in FPVs is to be diagnosed using the principal curve regression model. This methodology will be validated using the CMP tester at USF.The collaboration with industry partners will facilitate a broad dissemination of scientific and technological discoveries in semiconductor manufacturing. It will also assist a broad array of industry to achieve better process control and continuous variation reduction. The success of the project will promote teaching and learning through the development of interdisciplinary curricular materials, establishment of a CMP testbed and web-based virtual lab, and a close Industry-University collaboration. This provides opportunities to train a new highly skilled workforce in process control and quality improvement by exposing graduate/undergraduate/K-12 students to interdisciplinary training and novel methods of analyzing FPVs in micro/nano scale material removal processes. Women/minority students will be recruited through Bridges to Doctorate, Sloan fellowships, and REU (Research Experience for Undergraduate) supplements.