in many cases, the main cause of yield loss is a specific layout pattern that is difficult to manufacture and is prone to causing an open or short defect. This situation is getting worse with advanced technology nodes due to small feature sizes and complex manufacturing processes. Volume scan diagnosis results are a rich data source for identifying such yield limiting layout patterns, but a big challenge is how to deal with an enormously large number of potential layout patterns to be considered for analysis and how to avoid over fitting. In this paper we present enhancements to the previously published root cause deconvolution technique for analyzing volume scan diagnosis data that enables it to overcome this and correctly, and automatically, determine the right layout patterns causing systematic yield loss. Also presented is an application to industrial data where a layout pattern identified by the new technique was validated by physical root cause analysis to be the dominant yield loss mechanism.