Advanced Scan Diagnosis based Fault Isolation and Defect Identification for Yield Learning

Yield analysis of sub-micron devices has become an ever-increasing challenge. The difficulty is compounded by the lack of in-line inspection data as many companies adopt foundry or fab-less model. In this scenario failure analysis is becoming increasingly critical to help drive yields. Failure analysis is a process of fault isolation or a method of isolating failures as precisely as possible followed by identification of a physical defect. As the number of transistors and metal layers increase, the traditional fault isolation techniques are becoming less applicable and un-economical due to the amount of time needed to locate the physical defect. One solution to the yield analysis problem is scan diagnosis based fault isolation. Previous scan diagnosis based techniques were limited with little information about the type of fault and accuracy of diagnosis. With the introduction of new scan diagnosis algorithms it is now possible to not only isolate but to identify the type of fault as well as assigning an accuracy ranking, prior to any destructive analysis. This paper presents multiple case studies illustrating the application of scan diagnosis as an effective means to achieve yield enhancement. The advanced scan diagnostic tool utilized in this study provides information about the fault type as well as fault location. This information is used to focus failure analysis efforts toward a suspected defect, decreasing the cycle time required to determine root cause as well as increasing the over all success rate.