High-yield performance-efficient redundancy analysis for 2D memory  

作　　者：HUANG Tsung-Chu 

机构地区：[1]Department of Electronics Engineering National Changhua University of Education

出　　处：《Science China(Information Sciences)》2011年第8期1663-1676,共14页中国科学（信息科学）（英文版）

摘　　要：High-yield performance-efficient remapping architecture,repairing algorithms and redundancy analysis （HYPERA） are proposed for 2D memory.The proposed hypercube-based memory repair architecture consists of spare row-like subcubes with a modified ternary CAM with an address concentrator and a parallel sorter-like address concentrator.Generally,for an acceptable repair rate about 3% of spare subcubes and no more than 5% of hardware overhead are required. A modified Quine-McCluskey algorithm and the Essential Cube Pivoting algorithm are also developed for redundancy analysis.Almost 100% of repair rate can be obtained using only 32 equivalent rows under reasonable situations.Under less spare memory the repair rates of proposed approaches can be much higher than most results of previous work.High-yield performance-efficient remapping architecture,repairing algorithms and redundancy analysis （HYPERA） are proposed for 2D memory.The proposed hypercube-based memory repair architecture consists of spare row-like subcubes with a modified ternary CAM with an address concentrator and a parallel sorter-like address concentrator.Generally,for an acceptable repair rate about 3% of spare subcubes and no more than 5% of hardware overhead are required. A modified Quine-McCluskey algorithm and the Essential Cube Pivoting algorithm are also developed for redundancy analysis.Almost 100% of repair rate can be obtained using only 32 equivalent rows under reasonable situations.Under less spare memory the repair rates of proposed approaches can be much higher than most results of previous work.

关 键 词：memory repairing redundancy analysis remapping architecture HYPERCUBE cluster faults fault tollerant 

分 类 号：TP333.1[自动化与计算机技术—计算机系统结构] S831.4[自动化与计算机技术—计算机科学与技术]