Experiences in the use of evolutionary techniques for testing digital circuits
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, SPIE 1998 Annual Meeting
Invited paper
KEYWORDS: ATPG,
Approximate Methods,
Evolutionary Algorithms,
Gate-Level,
Genetic Algorithms,
Simulation-Based Approaches
ABSTRACT
The generation of test patterns for sequential circuits is one of the most challenging problems arising in the field of Computer-Aided Design for VLSI circuits. In the past decade, Genetic Algorithms have been deeply investigated as a possible approach: several algorithms have been described, and significant improvements have been proposed with respect to their original versions. As a result, Genetic Algorithm-based test pattern generators can now effectively compete with other methods, such as topological or symbolic ones. This paper discusses the advantages and disadvantages of GA-based approaches and describes GATTO, a state-of-the-art Genetic Algorithm-based test pattern generator. Other algorithms belonging to the same category are outlined as well. The paper puts GATTO and other GA-based tools in perspective, and shows that Evolutionary Computation techniques can successfully compete with more traditional approaches, or be integrated with them.
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[CSRe98] F. Corno, M. Sonza Reorda, M. Rebaudengo, "Experiences in the use of evolutionary techniques for testing digital circuits," Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, SPIE 1998 Annual Meeting