An Optimization Approach of Ant Colony Algorithm and Adaptive Genetic Algorithm for MCM Interconnect Test

Abstract

An optimization approach based on ant colony algorithm (ACA) and adaptive genetic algorithm (AGA) is presented for the Multi-chip Module (MCM) interconnect test generation problem in this paper. The pheromone updating rule and state transition rule of ACA is designed for automatic test generation by combing the characteristics of MCM interconnect test. AGA generates the initial candidate test vectors by utilizing genetic operator. In order to get the best test vector with the high fault coverage, ACA is employed to evolve the candidates generated by AGA. The international standard MCM benchmark circuit was used to verify the approach. Comparing with not only the evolutionary algorithms, but also the deterministic algorithms, simulation results demonstrate that the hybrid algorithm can achieve high fault coverage, compact test set and short execution time.