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A parallel built-in self-diagnostic method for nontraditional faults of embedded memory arrays

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4 Author(s)
V. Arora ; Dept. of Electr. & Comput. Eng. & Comput. Sci., Univ. of Cincinnati, OH, USA ; W. B. Jone ; D. C. Huang ; S. R. Das

In this paper, we propose a built-in self-diagnostic march-based algorithm that identifies faulty memory cells based on a recently introduced nontraditional fault model. It is developed based on the DiagRSMarch algorithm, which is a diagnostic algorithm to identify traditional faults for embedded memory arrays. A minimal set of additional operations is added to DiagRSMarch for identifying the nontraditional faults without affecting the diagnostic coverage of the traditional faults. The embedded memory arrays are accessed using a bidirectional serial interfacing architecture which minimizes the routing overhead introduced by the diagnosis hardware. Using the concepts of the bidirectional interfacing technique, parallel testing, and redundant-tolerant operations, the diagnostic process can be accomplished efficiently at-speed with minimal hardware overhead.

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:53 ,  Issue: 4 )