Improved overlay control using robust outlier removal methods

Overlay control is one of the most critical areas in advanced semiconductor processing. Maintaining optimal product disposition and control requires high quality data as an input. Outliers can contaminate lot statistics and negatively impact lot disposition and feedback control. Advanced outlier removal methods have been developed to minimize their impact on overlay data processing. Rejection methods in use today are generally based on metrology quality metrics, raw data statistics and/or residual data statistics. Shortcomings of typical methods include the inability to detect multiple outliers as well as the unnecessary rejection of valid data. As the semiconductor industry adopts high-order overlay modeling techniques, outlier rejection becomes more important than for linear modeling. In this paper we discuss the use of robust regression methods in order to more accurately eliminate outliers. We show the results of an extensive simulation study, as well as a case study with data from a semiconductor manufacturer.