电子学报 ›› 2022, Vol. 50 ›› Issue (1): 63-71.DOI: 10.12263/DZXB.20200399

所属专题: 长摘要论文

• 学术论文 • 上一篇    下一篇

结合结构特征基于测试集重排序的故障诊断方法

欧阳丹彤1,3, 刘扬2, 宋金彩2, 王浩然4, 张立明1,3   

  1. 1.吉林大学计算机科学与技术学院,吉林 长春 130012
    2.吉林大学软件学院,吉林 长春 130012
    3.符号计算与知识工程教育部重点实验室(吉林大学),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
  • 收稿日期:2020-04-27 修回日期:2021-01-04 出版日期:2022-01-25
    • 作者简介:
    • 欧阳丹彤 女,1968年出生于吉林省长春市.1998年毕业于吉林大学计算机科学系并获博士学位.教授、博士生导师.主要研究方向为自动推理与基于模型诊断. E-mail:ouyd@jlu.edu.cn
      刘 扬 男,1994年出生于内蒙古自治区巴彦淖尔市.2020年毕业于吉林大学软件学院并获硕士学位.主要研究方向为基于模型诊断和测试诊断. E-mail:ly2020@jlu.edu.cn
      张立明(通信作者) 男,1980年出生于吉林省榆树市.吉林大学博士.主要研究方向为基于模型诊断、可满足性问题和测试诊断. E-mail:limingzhang@jlu.edu.cn
    • 基金资助:
    • 国家自然科学基金 (62076108)

Fault Diagnosis Method Based on Test Set Reordering Combined with Structural Features

OUYANG Dan-tong1,3, LIU Yang2, SONG Jin-cai2, WANG Hao-ran4, ZHANG Li-ming1,3   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
    2.Software Institute, Jilin University, Changchun, Jilin 130012, China
    3.Key Laboratory of Symbolic Computation and Knowledge Engineering(Jilin University), Ministry of Education, Changchun, Jilin 130012, China
    4.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100000, China
  • Received:2020-04-27 Revised:2021-01-04 Online:2022-01-25 Published:2022-01-25
    • Supported by:
    • National Natural Science Foundation of China (62076108)

摘要:

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.

长摘要
随着现代半导体工业的迅猛发展,如今集成电路已经发展到由数十亿个晶体管组成的规模。电路的规模不断增大但电路尺寸却不断缩小,使得电路故障检测与诊断的难度和成本也随之不断增加。为了解决故障诊断效率相对低下的问题,本文研究如何能够快速、有效地诊断出电路中发生的故障。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(Reor⁃dering 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.

关键词: 故障诊断, 测试分数, 测试激励集重排序

Abstract:

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.

Extended Abstract
With the rapid development of the modern semiconductor industry, integrated circuits have now grown to the scale of billions of transistors. The scale of the circuit continues to increase, while the size of it continues to shrink, which makes it more difficult to detect fault and form diagnosis with higher cost. In order to solve the problem of relatively low efficiency of fault diagnosis, this paper focuses on how to diagnose faults in circuits quickly and effectively. Fault diagnosis is an important research direction in the field of integrated circuits which can solve candidate fault diagnosis based on test sets effectively. GTreord is a method with the highest diagnostic accuracy so far. Based on the structural characteristics between test excitation and candidate fault diagnosis solutions, this paper proposes a candidate diagnosis named RTDD of reordering test excitation sets combined with structural characteristics by analyzing the circuit fault output response. According to the different influence degrees of test on the generation of candidate fault diagnosis solution set, the concept 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 calculated and 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, results of the experiment 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 tests with the same diagnostic accuracy.

Key words: fault diagnosis, test score, test set reordering

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