A modern semiconductor manufacturing line is made of hundreds of sequential batch-processing stages. Each of these stages consists of many steps carried out by expensive tools, which are monitored by numerous sensors capable of sampling at intervals of seconds. The sensor readings of each run constitute profiles, which can include extremely drastic changes. The heterogeneous variations at different profile points are mainly due to on—off recipe actions at specific points. In addition, the analysis of these profiles is further complicated by long-term trends due to tool aging and short-term effects specific to the first wafer in a lot cycle. Statistical process control methods that fail to take these effects into consideration will lead to frequent false alarms. A systematic method is proposed to address these challenges. First, a reference profile is determined for each sensor variable that describes the on—off actions. Next, level shifts of these profiles in each step are established to capture and remove intrinsic variations due to long-term aging trends and the short-term first-wafer effects. The residuals are used to formulate a health index, and this index can be used to monitor the health of the equipment and detect faulty wafers efficiently.
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Lee et al. (2011) studied this question.