GPU-Based Redundancy Analysis Using Concurrent Evaluation

Tae Hyun Kim, Hayoung Lee, Sungho Kang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Redundancy analysis (RA) is essential for improving memory yield. The recent increase in memory size has made RA more complicated. This article presents graphics processing unit (GPU)-based RA using concurrent evaluation (GRACE), which is an efficient RA technique. In GRACE, to perform dynamic RA, memory faults found during the test are directly analyzed instead of being stored in the fault bitmap in the automatic test equipment (ATE). Therefore, RA is performed simultaneously with the memory test, and the RA latency is eliminated after the test time. Using the GPU, all possible repair cases are examined in parallel; thus, a high memory repair rate is achieved in a short period of time. Also, GRACE can be applied to practical environments where the structure of memory redundancy is complicated. Experimental results indicate that GRACE is faster than other ATE-based RA methods since it completes the RA almost simultaneously at the end of the test. Additionally, the repair rate of GRACE is always higher than those of the other RA methods.

Original languageEnglish
Article number8935214
Pages (from-to)805-817
Number of pages13
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Issue number3
Publication statusPublished - 2020 Mar

Bibliographical note

Publisher Copyright:
© 1993-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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