Predicting the Resolution of Scan Diagnosis.

Scan diagnosis has long been relied upon to provide localized defect suspects for a failing die using failing test cycle information for that die and design data. These suspects from scan diagnosis have been used to drive failure analysis (FA) to find the root cause of manufacturing yield loss. The fewer suspects that scan diagnosis produces (higher diagnosis resolution), the quicker and more efficient the FA cycle time. The gains observed are mainly due to the reduced need for fault isolation for these highly resolved diagnosis reports. Identifying design or test pattern related bottlenecks to diagnosis resolution earlier in the design cycle can be useful to anticipate the impact of a particular design on yield learning. In the technique described in this paper, we show how diagnosis resolution can be estimated from design data for both chain and logic defects. The detailed comparison of diagnostic metrics and resolution statistics from simulation and silicon results are presented. Overall, we observe strong correlation in the predicted resolution metrics and diagnosis quality.