Redundancy Analysis Optimization with Clustered Known Solutions for High Speed Repair

Hayoung Lee, Donghyun Han, Hogyeong Kim, Sungho Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the probability of fault occurrence increases with the advance of memory density and capacity, redundancy analysis (RA) is widely used for memory yield. However, the conventional RAs progress unnecessary solution search stages since they are applied per memory bank. It results in increase of the repair time. In this paper, redundancy analysis optimization with clustered known solutions (ROCK) is proposed for high speed repair. It progresses repair solution search considering multiple memory banks. During the repair solution search, RA using ROCK finds duplicated solution search stages based on a fault grouping method. After then, RA using ROCK enrolls the duplicated solution search stages as library and utilizes the library instead of progressing the duplicated solution search stages. It can highly reduce the repair time without any repair rate degradation.

Original languageEnglish
Title of host publicationProceedings - International SoC Design Conference, ISOCC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages51-52
Number of pages2
ISBN (Electronic)9781728183312
DOIs
Publication statusPublished - 2020 Oct 21
Event17th International System-on-Chip Design Conference, ISOCC 2020 - Yeosu, Korea, Republic of
Duration: 2020 Oct 212020 Oct 24

Publication series

NameProceedings - International SoC Design Conference, ISOCC 2020

Conference

Conference17th International System-on-Chip Design Conference, ISOCC 2020
Country/TerritoryKorea, Republic of
CityYeosu
Period20/10/2120/10/24

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea(MSIT) (No. 2019R1A2C3011079).

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Instrumentation
  • Artificial Intelligence
  • Hardware and Architecture

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