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New advances in diagnosis analysis technology make DDYA more valuable than ever before. If you haven\u2019t kept up with the technical literature or learned all about the range of products available, download this free technical paper<\/strong><\/a> for a great overview of state-of-the-art scan diagnosis and yield analysis technologies.<\/p>\n The paper was presented as a tutorial at the 2015 IEEE European Test Symposium by Mentor scientists. In it, the authors survey the recent advancements in DDYA, including application of different fault models and the related technologies that improve diagnosis accuracy and performance.<\/p>\n Scan diagnosis technologies include diagnosing failures in the scan chain, performing layout-aware logic diagnosis,\u00a0 diagnosing failures after test data compaction, and diagnosing other fault types (transition, hold-time, Iddq, compound defect, etc.) with Cell-aware diagnosis.<\/p>\n The paper also presents some DDYA best practices based on industrial experiences, and describes two sophisticated statistical techniques that greatly improve failure analysis (Figure 2), zonal analysis and root-cause decovolution.<\/p>\n![\"GE-What-to-Know](\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/48\/2016\/08\/GE-What-to-Know-Banner.jpg\")
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\nIf you worry about semiconductor yield challenges you may already be armed with newer technologies like diagnosis-driven yield analysis (DDYA), which can rapidly identify the root cause of yield loss and effectively separate design- and process-oriented yield loss (Figure 1). The return-on-investment in DDYA can be great; for example, Freescale published a case study in which they improved mature yield by 1.5% in a few weeks by using DDYA technology on 1300 failing die.<\/p>\n\u00a0
<\/a><\/h4>\nFigure 1. The DDYA flow.<\/h4>\n