Abstract
With test quality being an imperative, this paper presents a methodology on how to apply volume scan diagnosis - known from the field of yield learning - to the domain of test quality learning. Volume diagnosis allows to drastically accelerate the learning cycle. We give guidelines on how to improve test pattern generation strategies and try to answer which defects can be addressed deterministically with adequate fault models versus where probabilistic methods such as N-detect need be applied. The paper is based on a detailed analysis of scan diagnosis data from a production volume of well over one million devices of a 90 nm product.