Recovering from biased distribution of faulty cells in memory by reorganizing replacement regions through universal hashing

Jaeyung Jun, Kyu Hyun Choi, Hokwon Kim, Sang Ho Yu, Seon Wook Kim, Youngsun Han

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Recently, scaling down dynamic random access memory (DRAM) has become more of a challenge, with more faults than before and a significant degradation in yield. To improve the yield in DRAM, a redundancy repair technique with intra-subarray replacement has been extensively employed to replace faulty elements (i.e., rows or columns with defective cells) with spare elements in each subarray. Unfortunately, such technique cannot efficiently handle a biased distribution of faulty cells because each subarray has a fixed number of spare elements. In this article, we propose a novel redundancy repair technique that uses a hashing method to solve this problem. Our hashing technique reorganizes replacement regions by changing the way in which their replacement information is referred, thus making faulty cells become evenly distributed to the regions. We also propose a fast repair algorithm to find the best hash function among all possible candidates. Even if our approach requires little hardware overhead, it significantly improves the yield when compared with conventional redundancy techniques. In particular, the results of our experiment show that our technique saves spare elements by about 57% and 55% for a yield of 99% at BER 1e-6 and 5e-7, respectively.

Original languageEnglish
Article number16
JournalACM Transactions on Design Automation of Electronic Systems
Issue number2
Publication statusPublished - 2017 Sept


  • DRAM fault recovery
  • DRAM yield
  • Fault recovery algorithm
  • Universal hashing

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


Dive into the research topics of 'Recovering from biased distribution of faulty cells in memory by reorganizing replacement regions through universal hashing'. Together they form a unique fingerprint.

Cite this