用KM算法增强测试集的频谱主分量

在集成电路测试领域常常需要对测试集和测试响应进行频谱分析,计算其频谱主分量,用于指导测试产生和进行测试数据压缩等。提出一种用KM(Kuhn-Munkras)算法增强测试集频谱主分量的方法,先根据测试集和其频谱主分量矩阵构建二分图模型和权值矩阵,把增强频谱主分量的问题转化为二分图的匹配问题,然后用KM算法求解。根据匹配关系调整测试集中测试向量的顺序后,频谱主分量和测试集的相关性增加,频谱主分量得到增强。在ISCAS-89基准电路测试集的实验表明,测试集排序后,其频谱主分量的相关性提高了19.05%,测试集残差FDR编码压缩率提高了4.59%。

Enhance prominent spectral component of test set by using KM algorithm

In the field of integrated circuit testing,in order to improve the test data compression ratio and test generation,it is often necessary to do spectral analysis of the test set and test response and calculate their prominent spectral component.A method is proposed to enhance the prominent spectral component of test set by using KM (Kuhn-Munkras) algorithm.Based on the test set and its prominent spectral component,a bipartite graph and a weighting matrix are constructed.The problem of the enhancement of prominent spectral component is transformed into a bipartite graph matching problem,and then be solved by KM algorithm.After the order adjustment of test set according to the matching relationship,the correlation between prominent component and test set is increased,and the prominent spectral component is enhanced.In this paper,the experimental results about the test set of the ISCAS-89 benchmark circuits show that the coefficient of the sorted test can increase by 19.05％ on average,and the test set residue compression ratio basis on FDR (frequency-directed run-length) code can increase by 4.59％ on average.