嵌入式存储器阵列内建自修复的神经计算

[1989] The Nineteenth International Symposium on Fault-Tolerant Computing. Digest of Papers Pub Date : 1989-06-21 DOI:10.1109/FTCS.1989.105623

P. Mazumder, Jih-Shyr Yih

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引用次数: 7

摘要

演示了如何将记忆修复问题的目标函数表示为神经网络的能量函数，以及如何利用神经网络的收敛性来寻找近最优解。利用神经网络开发了两种算法，并将其性能与商业上使用的“修复最多”算法进行了比较。对于随机生成的缺陷模式，所提出的具有爬坡能力的算法在98%的情况下成功修复了存储阵列，而大多数算法的修复率为20%。

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Neural computing for built-in self-repair of embedded memory arrays

A demonstration is presented of how to represent the objective function of the memory repair problem as a neural network energy function, and how to utilize the neural net's convergence property to find near-optimal solutions. Two algorithms have been developed using a neural network, and their performance is compared with the 'repair most' algorithm that is used commercially. For randomly generated defect patterns, the proposed algorithm with a hill-climbing capability has been found to be successful in repairing memory arrays in 98% of the cases, as opposed to the repair most algorithm's 20% of cases.<>

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来源期刊

[1989] The Nineteenth International Symposium on Fault-Tolerant Computing. Digest of Papers

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