EWMA/SD: an end-of-line SPC scheme to monitor sequence-disordered data [semiconductor manufacturing]

In this paper, we focus on the design issues of applying an SPC control chart to end-of-line wafer acceptance test (WAT) data. Since the sequence of end-of-line data is not the same as the sequence in each process step, an abnormal trend in any of the process steps is more difficult to detect based on the end-of-line data than based on single process data (if available). To overcome this deficiency, we propose an exponentially weighted moving average method for sequence-disordered data (EWMA/SD). The basic idea is that the moving average can smooth out the sequence-disordered effect and weighting factors allow us to choose an effective window size so that the underlying trend can be seen. It is different from the traditional EWMA method as it has the capability of handling sequence-disordered data. An end-of-line trend detection system has been developed for validation of the method, which consists of three modules: a run length distribution generator, an optimal parameter generator, and an EWMA/SD control chart. Based on process characteristics, the corresponding run lengths of an EWMA/SD control chart for different parameter vectors are derived by the Markov chain approach. The optimal parameter vector is chosen as the one which meets the requirement for maximum false alarm rate and maximizes the detection speed at the same time. Results of simulation and field data validation show that EWMA/SD is able to smooth out the sequence-disordered data, be sensitive to process changes, and be robust to background noise.