Screening for known good die (KGD) based on defect clustering: an experimental study

Die screening based on the locality of defects has long been informally practised in the industry whereby dice from wafers, or parts of the wafer, that display high defect levels are discarded. More recently this approach has been refined such that test results for neighbouring dice on the wafer are also considered in evaluating test results for a particular die. It has been shown in principle, using negative binomial statistics for defect distributions on wafers, that such an approach can much better optimize test costs and screen for low defect levels in bare dice and packaged chips. In this paper we present, for the first time, experimental test data to demonstrate the effectiveness of this new approach. Our results are based on extensive testing of 4784 dice on 23 wafers from an IBM process. We show that bare die screening based on defect clustering considerations can significantly reduce defect levels in dice that pass wafer probe tests. This approach also has the potential to screen out burn-in failures. Thus it offers new low cost strategies for delivering high quality “known- good” die (KGD) for MCM applications