结合结构特征基于测试集重排序的故障诊断方法
Fault Diagnosis Method Based on Test Set Reordering Combined with Structural Features

欧阳丹彤 ^1,2 刘扬 ³ 宋金彩 ³ 王浩然 ⁴ 张立明 ^1,2 ^*

文摘	故障诊断是集成电路领域中的重要研究方向,基于测试激励集方法求解候选故障诊断是目前较为高效的诊断方法,而GTreord是目前具有较高诊断准确性的方法.在对GTreord方法深入研究的基础上,本文依据测试激励与候选故障诊断解之间的结构特征,通过分析电路故障输出响应,提出结合结构特征的测试激励集重排序的候选诊断(Reordering Test Default Diagnosis,RTDD)方法.根据测试激励对生成候选故障诊断解集合的影响程度的不同,提出测试分数概念;通过比较电路的实际故障输出响应、无故障输出响应、模型故障输出响应,计算出测试激励的测试分数.测试激励集依据测试分数进行重排序,并将重排序后的测试激励集用于故障诊断.实验结果表明,与GTreord方法相比,RTDD方法提高了测试激励集重排序的效率,求解时间提高1~4个数量级;此外,在保障同样诊断准确性的情况下,RTDD方法有效减少了所需测试的激励个数.
其他语种文摘	Fault diagnosis is a main direction in the research of integrated circuits which can solve candidate fault diagnosis based on test sets effectively. GTreord is a method with the best diagnostic accuracy currently. Based on deep analysis of the GTreord method, in this paper, a candidate diagnosis solution method based on test set reordering is proposed, referred as RTDD. RTDD method is based on the structural characteristics between the test and the candidate fault diagnosis solutions and analyzes the circuit fault output response. According to the different influence degrees of test on the generation of candidate fault diagnosis solution set, the notion of test score is presented. By comparing the actual fault output response, non-fault output response and model fault output response of the circuit, the test score of the test is obtained. The test set is reordered according to the test score, and the reordered test is applied to the fault diagnosis. Compared with the GTreord method, the experiments show that RTDD method improves the efficiency of test set reordering, and the running time is improved by 1-4 orders of magnitude. In addition, RTDD method effectively reduces the number of required test under the same diagnostic accuracy.
来源	电子学报 ,2022,50(1):63-71 【核心库】
DOI	10.12263/DZXB.20200399
关键词	故障诊断 ; 测试分数 ; 测试激励集重排序
地址	1. 吉林大学计算机科学与技术学院, 吉林, 长春, 130012 2. 吉林大学, 符号计算与知识工程教育部重点实验室, 吉林, 长春, 130012 3. 吉林大学软件学院, 吉林, 长春, 130012 4. 中国科学院大学计算机科学与技术学院, 北京, 100000
语种	中文
文献类型	研究性论文
ISSN	0372-2112
学科	自动化技术、计算机技术
基金	国家自然科学基金
文献收藏号	CSCD:7169558