A Locating Method for Reliability-Critical Gates with a Parallel-Structured Genetic Algorithm

The reliability allowance of circuits tends to decrease with the increase of circuit integration and the application of new technology and materials, and the hardening strategy oriented toward gates is an effective technology for improving the circuit reliability of the current situations. Therefore, a parallel-structured genetic algorithm (GA), PGA, is proposed in this paper to locate reliability-critical gates to successfully perform targeted hardening. Firstly, we design a binary coding method for reliability-critical gates and build an ordered initial population consisting of dominant individuals to improve the quality of the initial population. Secondly, we construct an embedded parallel operation loop for directional crossover and directional mutation to compensate for the deficiency of the poor local search of the GA. Thirdly, for combination with a diversity protection strategy for the population, we design an elitism retention based selection method to boost the convergence speed and avoid being trapped by a local optimum. Finally, we present an ordered identification method oriented toward reliability-critical gates using a scoring mechanism to retain the potential optimal solutions in each round to improve the robustness of the proposed locating method. The simulation results on benchmark circuits show that the proposed method PGA is an efficient locating method for reliability-critical gates in terms of accuracy and convergence speed.