PLAtestGA: A CNF-Satisfiability Problem for the Generation of Test Vectors for Missing Faults in VLSI Circuits

Authors:
Alfredo Cruz
Affiliations:
-
Venue:
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Year:
2000

Citing 6
Cited 0

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Multiple Fault Detection in Programmable Logic Arrays

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Abstract

An evolutionary algorithm (EA) approach is used in the development of a test vector generation application for single and multiple fault detection of growth faults in Programmable Logic Arrays (PLA). Evolutionary algorithms are search and optimization procedures that find their origin and inspiration in the biological world. In this paper, we apply the genetic operators to the CNF-satisfiability problem for the generation of test vectors for growth faults. CNF has several advantages, there are not dependencies between bits: any change would result in a legal (meaning) vector (either a minterm or a maxterm). Thus we can apply mutations and crossover without any need for decoders or repair algorithms. The crossover operation unlike previous operators used in PLA test generation, does not use lookups or backtracking.