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Abstract:
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This thesis presents the modeling of tool data produced during ion implantation for the prediction of wafer sheet resistance . In this work , we will use various statistical techniques to address challenges due to the nature of equipment data : high dimensionality , colinearity , parameter interactions , and non -linearities . The emphasis will be data integrity , variable selection , and model building methods . Different variable selection and modeling techniques will be evaluated using an industrial data set . Ion implant processes are fast and depending on the monitoring frequency of the equipment , late detection of a process shift could lead to the loss of a significant amount of product . The main objective of the research presented in this thesis is to identify any ion implant parameters that can be used to formulate a virtual metrology model . The virtual metrology model would then be used for process monitoring to ensure stable processing conditions and consequent yield guarantees . |