摘要
针对目前集成电路测试复杂度的不断增加,导致测试成本不断攀升的问题,提出一种可靠而有效的测试集优化方法。通过k均值(K-means)聚类对原始测试集中的特征进行聚类筛选,然后采用改进的mRMR算法,分段式引入特征之间冗余性权重因子,用以权衡特征相关性和冗余性的度量,同时插入了SVM交叉验证,强化了测试模式选择的准确性。在保证故障覆盖率基本不变的情况下,达到减少原始测试集维数的目的。对ISCAS89电路实验表明,该文方法将原始测试集的测试模式进行大量的精简,既保证测试质量,也极大地优化了测试集,进行冗余消除和排序后的测试流程缩短了40.43%的测试时间,提升了测试效率,降低了测试成本。
The complexity of current IC test is increasing,which leads to rising test cost.To address this issue,a reliable and effective test set optimization method is proposed in this article.The features in the original test set are filtered by clustering with K-means clustering.Then,a modified mRMR algorithm is used to introduce redundancy weighting factors among features by using a segmented formula to weigh the feature relevance and redundancy metrics.Meanwhile,the SVM cross-validation is inserted to reinforce the accuracy of the test pattern selection.The reduction in the number of dimensions of the original test set is achieved while ensuring that the fault coverage remains largely unchanged.Experiments on the ISCAS89 circuit show that the proposed method takes the test patterns of the original test set and streamlines them considerably.This method ensures test quality and also optimizes the test set.The test flow after redundancy elimination and sequencing reduces test time by 40.43%,improves test efficiency and reduces test cost.
作者
詹文法
张鲁萍
江健生
Zhan Wenfa;Zhang Luping;Jiang Jiansheng(School of Electronic Engineering and Intelligent Manufacturing,Anqing Normal University,Anqing 246133,China;School of Computer and Information,Anqing Normal University,Anqing 246133,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2023年第3期155-162,共8页
Chinese Journal of Scientific Instrument
基金
安徽省高校协同创新项目(GXXT-2019-030)
安徽省技术带头人及后备人选(gxbjZD2016075,2015H053)
国家自然科学基金(61306046,61640421)项目资助。
