Density-oriented diagnostic data compression strategy for characterization of embedded memories in Automotive Systems-on-Chip

Embedded System-on-Chip (SoC) memory requirements in the Automotive industry are constantly growing. For this reason, memories occupy a significant part of Automotive SoC's die area, increasing the defect probability inside the embedded storage. Automotive SoC manufacturers need to deeply test their embedded memories as they are one of the significant contributors to the yield of their devices. The test effort increases for the characterization of new technologies and new families of devices that need to be characterized by the manufacturers. These tests generate a massive quantity of diagnostic information that is incredibly valuable for designers and technology experts. This diagnostic information can be analyzed to identify and correct possible weaknesses and misbehavior. The easiest way to collect memory diagnostic information consists of failure bitmaps in which each fault is saved as coordinates. This method is the simplest solution to implement. However, logging the coordinates of every fault may generate an unmanageable quantity of data. This problem is exacerbated when there is an on-chip limitation on the amount of data that can be saved or transmitted to the external world. This paper presents an optimized on-chip compression algorithm that allows to reduce the required on-chip memory to store diagnostic information during embedded memory testing. This solution allows the reconstruction of a failure bitmap, generating a topological representation of the density of the failings bits in the embedded on-chip memory. The proposed approach effectively reduces the used storage to a fraction with respect to the one used by the original failing bitmap. The algorithm uses a coordinates-based approach, in which the memory is logically divided into equally divided sectors. The small time overhead introduced by the algorithm is compensated by the ability to achieve optimal space utilization.