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A new built-in self-repair approach to VLSI memory yield enhancement by using neural-type circuits

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2 Author(s)
Mazumder, P. ; Michigan Univ., Ann Arbor, MI, USA ; Jih, Y.-S.

It is shown how to represent the objective function of the memory repair problem as a neural-network energy function, and how to exploit the neural network's convergence property for deriving optimal repair solutions. Two algorithms have been developed using a neural network, and their performances are compared with that of the repair most (RM) algorithm. For randomly generated defect patterns, a proposed algorithm with a hill-climbing capability successfully repaired memory arrays in 98% cases, as opposed to RMs 20% cases. It is demonstrated how, by using very small silicon overhead, one can implement this algorithm in hardware within a VLSI chip for built in self repair (BISR) of memory arrays. The proposed auto-repair approach is shown to improve the VLSI chip yield by a significant factor, and it can also improve the life span of the chip by automatically restructuring its memory arrays in the event of sporadic cell failures during the field use

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Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on  (Volume:12 ,  Issue: 1 )