| 陈佳楠,马永涛,李 松,刘 丰.多目标优化的多存储器内建自测试[J].电子测量与仪器学报,2020,34(1):193-200 |
| 多目标优化的多存储器内建自测试 |
| Multi memory built in self test based on multi objective optimization |
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| DOI： |
| 中文关键词: 存储器 共享内建自测试 多目标优化 聚类 遗传算法 |
| 英文关键词:memory shared built in self test multi objective optimization cluster genetic algorithm |
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| 摘要点击次数: 398 |
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| 中文摘要: |
| 片上系统中含有大量的存储器，常使用共享内建自测试电路的方法测试。内建自测试电路的插入过程受到片上系统的面积开销、测试功耗与测试时间的约束。针对这个问题，将多存储器内建自测试建模为多目标优化问题，并提出一种多目标聚类遗传退火算法。该算法在遗传算法的基础上，通过存储器聚类获得存储器兼容组，采用启发式方法获得高质量初始解，提出一种多约束条件下不同权重的目标函数，对较优个体采用模拟退火算法规避局部最优解风险。实验结果表明,该算法比遗传算法性能更优，获得存储器组解进行测试，比现有方法测试功耗降低113%，或测试时间降低487%，节省了片上测试资源与测试时间。 |
| 英文摘要: |
| The system on chip (SOC) contained a large amount of embedded memories, which were tested by a method of sharing built in self test circuits. The insertion process of the built in self test circuit was limited by the area overhead, test power and test time of the SOC. Aiming at this problem, the multi memory built in self test was modeled as a multi objective optimization problem, and a multi objective clustering genetic anneal algorithm was proposed. Based on the genetic algorithm, the algorithm obtained the memory compatible group through memory clustering, adopted the heuristic method to obtain the high quality initial solution, proposed an objective function with different weights under multiple constraints, and used the simulated annealing algorithm to evade better individuals to avoid local optimal solution risk. The results show that the proposed algorithm performs better than the genetic algorithm, and obtain memory solutions for testing, which reduces the power consumption by 113% or the test time by 487%, saving on chip test resources and test time. |
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