Biblio

Spin-transfer torque magnetoresistive random-access memories (STT-MRAMs) are gradually superseding conventional SRAMs as last-level cache in System-on-Chip designs. Their manufacturing process includes trimming a reference resistance in STT-MRAM modules to reliably determine the logic values of 0 and 1 during read operations. Typically, an on-chip trimming routine consists of multiple runs of a test algorithm with different settings of a trimming port. It may inherently produce a large number of mismatches. Diagnosis of such a sizeable volume of errors by means of existing memory built-in self-test (MBIST) schemes is either infeasible or a time-consuming and expensive process. In this paper, we propose a new memory fault diagnosis scheme capable of handling STT-MRAM-specific error rates in an efficient manner. It relies on a convolutional reduction of memory outputs and continuous shifting of the resultant data to a tester through a few output channels that are typically available in designs using an on-chip test compression technology, such as the embedded deterministic test. It is shown that processing the STT-MRAM output by using a convolutional compactor is a preferable solution for this type of applications, as it provides a high diagnostic resolution while incurring a low hardware overhead over traditional MBIST logic.