Empirical models based on real-time equipment signals are used to predict the outcome (e.g., etch rates and uniformity) of each wafer during and after plasma processing. Three regression and one neural network modeling methods were investigated. The models are verified on data collected several weeks after the initial experiment, demonstrating that the models built with real-time data survive small changes in the machine due to normal operation and maintenance. The predictive capability can be used to assess the quality of the wafers after processing, thereby ensuring that only wafers worth processing continue down the fabrication line. Future applications include real-time evaluation of wafer features and economical run-to-run control.
Competition in the semiconductor industry is forcing manufacturers to continuously improve the capability of their equipment. The analysis of real-time sensor data from semiconductor manufacturing equipment presents the opportunity to reduce the cost of ownership of the equipment. Previous work by the authors showed that time series filtering in combination with multivariate analysis techniques can be utilized to perform statistical process control, and thereby generate real-time alarms in the case of equipment malfunction. A more robust version of this fault detection algorithm is presented. The algorithm is implemented through RTSPC, a software utility which collects real-time sensor data from the equipment and generates realtime alarms. Examples of alarm generation using RTSPC on a plasma etcher are presented.
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