Biblio

Software based scan diagnosis is the de facto method for debugging logic scan failures. Physical analysis success rate is high on dies diagnosed with maximum score, one symptom, one suspect and shorter net. This poses a limitation on maximum utilization of scan diagnosis data for PFA. There have been several attempts to combine dynamic fault isolation techniques with scan diagnosis results to enhance the utilization and success rate. However, it is not a feasible approach for foundry due to limited product design and test knowledge and hardware requirements such as probe card and tester. Suitable for a foundry, an enhanced diagnosis-driven analysis scheme was proposed in [1] that classifies the failures as frontend-of-line (FEOL) and backend-of-line (BEOL) improving the die selection process for PFA. In this paper, static NIR PEM and defect prediction approach are applied on dies that are already classified as FEOL and BEOL failures yet considered unsuitable for PFA due to low score, multiple symptoms, and suspects. Successful case studies are highlighted to showcase the effectiveness of using static NIR PEM as the next level screening process to further maximize the scan diagnosis data utilization.

GDSII layouts of IP-confidential products are heavily controlled and access is only granted to certain privileged personnel. Failure analysts are generally excluded. Without guidance from GDSII, failure analysis, specifically physical inspection based on fault isolation findings cannot proceed. To overcome this challenge, we develop an automated approach that enables image snapshots relevant to failure analysts to be furnished without compromising the confidentiality of the GDSII content in this paper. Modules built are executed to trace the suspected nets and extract them into multiple images of different pre-defined frame specifications to facilitate failure analysis.