Recently the semiconductor industry has been alerted by hyperscaler companies reporting impact of field errors in megascale datacenters. They tend to be elusive and very difficult to detect until they affect a particular application several days or months after the IC has been deployed in a fleet. Although the cause of such errors can be manifold, ranging from test escapes and design marginalities to design bugs, there is a consensus across the industry that they usually can be traced back to timing-related issues, as the performance of transistors changes over time or at certain environmental conditions while running specific software workloads. While there is ongoing work to study some of those defects and to explore techniques preventing such post-manufacturing test escapes, it also highlights the need for IC monitoring so that such defects are detected in the field, thereby reducing application failures. The paper demonstrates a successful application of the Streaming Scan Network technology to run in-fleet deterministic scan test on an ultralarge industrial multi-chiplet design at Amazon Web Services (AWS) cloud computing platform. One of the key advantages of the presented technology is its ability to use the same infrastructure to perform both manufacturing and in-field tests. A silicon implementation along with test power analysis are also presented.

We present a new distributed association rule mining (D-ARM) algorithm that demonstrates superlinear speedup with the number of computing nodes. The algorithm is the first D-ARM algorithm to perform a single scan over the database. As such, its performance is unmatched by any previous algorithm. Scale-up experiments over standard synthetic benchmarks demonstrate stable run time regardless of the number of computers. Theoretical analysis reveals a tighter bound on error probability than the one shown in the corresponding sequential algorithm.