Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Minimal cost one-dimensional linear hybrid cellular automata of degree through 500
Journal of Electronic Testing: Theory and Applications
The selfish gene algorithm: a new evolutionary optimization strategy
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Deterministic Built-In Self-Test Generator Based on Cellular Automata Structures
IEEE Transactions on Computers
Cellular automata for deterministic sequential test pattern generation
VTS '97 Proceedings of the 15th IEEE VLSI Test Symposium
VTS '98 Proceedings of the 16th IEEE VLSI Test Symposium
A Genetic Algortithm for Automatic Generation of Test Logic for Digital Circuits
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Cellular Automata-Based Development of Combinational and Polymorphic Circuits: A Comparative Study
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
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Testing is a key issue in the design and production of digital circuits: the adoption of BIST (Built-In Self-Test) techniques is increasingly popular, but requires efficient algorithms for the generation of the logic which generates the test vectors applied to the Unit Under Test. This paper addresses the issue of identifying a Cellular Automaton able to generate input patterns to detect stuck-at faults inside a Finite State Machine (FSM) circuit. Previous results already proposed a solution based on a Genetic Algorithm which directly identifies a Cellular Automaton able to reach good Fault Coverage of the stuck-at faults. However, such method requires 2-bit cells in the Cellular Automaton, thus resulting in a high area overhead. This paper presents a new solution, with an area occupation limited to 1 bit per cell; the improved results are possible due to the adoption of a new optimization algorithm, the Selfish Gene algorithm. Experimental results are provided, which show that in most of the standard benchmark circuits the Cellular Automaton selected by the Selfish Gene algorithm is able to reach a Fault Coverage higher that what can be obtained with current engineering practice with comparable area occupation.