A Search Algorithm for the Worst Operation Scenario of a Cross-Point Phase-Change Memory Utilizing Particle Swarm Optimization

Abstract

In this paper, we propose a search algorithm to find the worst operation scenario of a cross-point array of a phase-change random access memory to enable a precise read margin evaluation. The search algorithm utilizes a particle swarm optimization method to find the worst scenario quickly and efficiently. In an experiment, the proposed algorithm improves the search speed by 39.3x compared with the previous algorithm. With the improved search speed, the proposed algorithm could find the worst operation scenarios of large arrays whose worst operation scenarios had been only guessed before. In the experiment with a large array, the proposed algorithm proved that the worst high-resistance state read current can be 36x larger than the previous best guess. In the reliability test, the evaluation error of the worst read current found by the proposed algorithm is less than 0.2% with 99% probability. These results show that the proposed search algorithm can improve the precision and efficiency of the read margin evaluation in designing a cross-point phase-change memory array.