Sequential screening in semiconductor manufacturing. 2. Exploiting lot-to-lot variability

This paper addresses the same quality management problem as Longtin, Wein and Welsch, except that here screening is performed at the wafer level, rather than at the chip level. An empirical Bayes approach is employed: The number of bad chips on a wafer is assumed to be a gamma random variable, where the scale parameter is unknown and varies from lot to lot according to another gamma distribution. We fit the yield model to industrial data and test the optimal policy on these data. The numerical results suggest that screening at the chip level, as in Longtin, Wein and Welsch, is significantly more profitable than screening at the wafer level.