A large end-to-end database was created by joining an IC test factory database with a board contract manufacturer (CM) database using the unique electrical identity fused into the IC and read at IC and CM test steps. A new rank correlation method was used to measure statistical dependencies of IC failure rates in the CM factory on failure rates and attributes measured in the IC factory. IC factory dependency, including radial wafer symmetry, was seen in the CM data providing opportunity to improve CM failure rate at the cost of IC yield. A special kind of receiver operating characteristic (ROC) was constructed to quantify the IC factory yield cost of CM failure rate improvements for hypothetical IC factory screens additional to the screens which generated the database. A screen based on RAM fuse repair count was found to be better than one based on blocking of edge dies.

Variable bit retention time observed in a 65-nm dynamic random access memory (DRAM) case study will cause miscorrelation between retention times occurring in Test and Use. Conventional multivariate normal statistics cannot adequately model this miscorrelation. A more general copula-based modeling approach, widely used in financial and actuarial modeling, solves this problem. The DRAM case study shows by example how to use copula models in test applications. The method includes acquiring data using a test vehicle, fitting the data to a copula-based statistical model, and then using the model to compute producer- and customer-oriented figures of merit of a product, different from the test vehicle. Different array sizes, fault tolerance schemes, test coverage, end-use (datasheet), and test condition specifications of the product are modeled.