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Identification and test generation for primitive faults

Authors: Angela Krstic; Kwang-Ting Cheng; Srimat T. Chakradhar;

Identification and test generation for primitive faults

Abstract

We propose a new method to identify and test primitive faults in combinational circuits described as multi-level or two-level netlists. A primitive fault is a multiple path delay fault for which none of the single paths contained in the fault is robustly or non-robustly testable while the presence of the fault can degrade the circuit performance. Identification and testing of primitive faults is important for at least two reasons: (1) a large percentage of paths in production circuits remain untestable under the single-path delay fault model, (2) distributed manufacturing defects usually adversely affect more than one path and these defects can be detected only by analyzing multiple affected paths. The single-path delay faults contained in a primitive fault have to merge at some gate(s). Our methodology for identifying primitive faults can quickly (1) rule out a large number of gates as possible merging points for primitive faults, and (2) prune the combination of paths that can never belong long any primitive fault. Our identification procedure also finds a test for the fault. We present a complete algorithm for identifying and testing double path delay faults. This procedure can be extended to identify primitive faults consisting of three or more paths. Experimental results on several multi-level combinational benchmark circuits are included to demonstrate the usefulness of our technique.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Average
Top 10%
Top 10%
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