中大學術數位典藏-NCU Institutional Repository-提供博碩士論文、考古題、期刊論文、研究計畫等下載:Item 987654321/108493
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 94201/94201 (100%)
Visitors : 81697155      Online Users : 3167
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/108493


    Title: A local parallel search approach for memory failure pattern identification
    Authors: 鄭政誠;Lin, Bing-Yang;Wu, Cheng-Wen;Lee, Mincent;Lin, Hung-Chih;Peng, Ching-Nen;Wang, Min-Jer
    Contributors: 文學院歷史研究所
    Keywords: Amnesia;Arrays;CMOS;Data mining;Design engineering;Failure;Failure analysis;failure pattern identification;Inspection;Logic circuits;Manuals;memory diagnosis;Memory management;memory testing;Sparse matrices;Tasks;yield improvement
    Date: 2016-03-01
    Issue Date: 2026-04-23 14:51:35 (UTC+8)
    Publisher: IEEE Computer Society;New York: IEEE
    Abstract: 摘要: Due to more aggressive design rules adopted by memories than logic circuits, memories have been considered as the major technology driver of advanced logic circuits, so far as CMOS process technology is concerned. Memory failure pattern identification therefore is important, and is traditionally considered a key task that can help improve the efficiency of memory diagnosis and failure analysis. Critical failure patterns (that are the yield killers), however, may change in different memory designs and process technologies. It is difficult to identify critical failure patterns from high-volume memory failure bitmaps if they are not predefined. To solve this problem, we propose a local parallel search algorithm for efficient memory failure pattern identification. In addition, the proposed system integrates the defect-spectrum-based and coordinate-distance-based methods to identify critical memory failure patterns from a large amount of memory failure bitmaps automatically, even if they are not defined in advance. In our experiment for 132,488 4-MB memory failure bitmaps, the proposed system can automatically identify six critical yet undefined failure patterns in minutes, in addition to all known patterns. In comparison, the state-of-the-art commercial tools need manual inspection of the memory failure bitmaps to identify the same failure patterns.
    其他題名: TC
    出版者: New York: IEEE
    出版日期: 2016-03-01
    出處: IEEE transactions on computers, 2016-03, Vol.65 (3), p.770-780
    資源來源: IEEE Electronic Library (IEL)
    版權: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016
    識別號: ISSN: 0018-9340
    識別號: EISSN: 1557-9956
    識別號: DOI: 10.1109/TC.2015.2462820
    識別號: CODEN: ITCOB4
    Appears in Collections:[Graduate Institute of History ] journal & Dissertation

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML25View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 隱私權政策聲明