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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

Published in:

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

Date of Publication:

Jan 1993

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