Defect Detection from Visual Abnormalities in Manufacturing Process Using IDDQ

被引:0
|
作者
Masaru Sanada
机构
[1] Analysis Technology Development Division,
来源
关键词
fault diagnosis; manufacturing; various current; visual abnormalities;
D O I
暂无
中图分类号
学科分类号
摘要
Abnormal IDDQ (Quiescent VDD supply current) indicates the existence of physical damage in a circuit. Using this phenomenon, a CAD-based fault diagnosis technology has been developed to enhance the manufacturing yield of logic LSI. This method to detect the fatal defect fragments in several abnormalities identified with wafer inspection apparatus includes a way to separate various leakage faults, and to define the diagnosis area encircling the abnormal portions. The proposed technique progressively narrows the faulty area by using logic simulation to extract the logic states of the diagnosis area, and by locating test vectors related to abnormal IDDQ. The fundamental diagnosis way employs the comparative operation of each circuit element to determine whether the same logic state with abnormal IDDQ exists in normal logic state or not.
引用
收藏
页码:275 / 281
页数:6
相关论文
共 50 条
  • [31] Visual detection of machining damage on aerospace aluminium alloys during manufacturing process
    Ma, Yanlong
    Liao, Yi
    ADVANCED RESEARCH ON APPLIED MECHANICS AND MANUFACTURING SYSTEM, 2013, 252 : 302 - +
  • [32] A novel data mining approach for defect detection in the printed circuit board manufacturing process
    Bártová B.
    Bína V.
    Engineering Management in Production and Services, 2022, 14 (02) : 13 - 25
  • [33] Shorter failure analysis using a new application of IDDQ for defect localization in ICs
    Desplats, R
    Bertrand, B
    Perdu, P
    Benbrik, J
    Marc, F
    Danto, Y
    MICROELECTRONIC MANUFACTURING YIELD, RELIABILITY, AND FAILURE ANALYSIS IV, 1998, 3510 : 30 - 36
  • [34] Autonomous Visual Detection of Defects from Battery Electrode Manufacturing
    Choudhary, Nirmal
    Clever, Henning
    Ludwigs, Robert
    Rath, Michael
    Gannouni, Aymen
    Schmetz, Arno
    Huelsmann, Tom
    Sawodny, Julia
    Fischer, Leon
    Kampker, Achim
    Fleischer, Juergen
    Stein, Helge S.
    ADVANCED INTELLIGENT SYSTEMS, 2022, 4 (12)
  • [35] Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images
    Subramaniam, Sulochana
    Kanfoud, Jamil
    Gan, Tat-Hean
    MACHINES, 2022, 10 (10)
  • [36] Managing risk as a resource using the defect detection and prevention process
    Cornford, SL
    PROBABILISTIC SAFETY ASSESSMENT AND MANAGEMENT (PSAM 4), VOLS 1-4, 1998, : 1609 - 1614
  • [37] Design and manufacturing process of skull defect prosthesis
    Szakal, Z
    Zsoldos, I
    Csernátony, Z
    CROSS-DISCIPLINARY APPLIED RESEARCH IN MATERIALS SCIENCE AND TECHNOLOGY, 2005, 480 : 641 - 644
  • [38] Intentional Weld Defect Process: From Manufacturing by Robotic Welding Machine to Inspection Using TFM Phased Array
    Javadi, Yashar
    Vasilev, Momchil
    MacLeod, Charles N.
    Pierce, Stephen G.
    Su, Riliang
    Mineo, Carmelo
    Dziewierz, Jerzy
    Gachagan, Anthony
    45TH ANNUAL REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOL 38, 2019, 2102
  • [39] A novel manufacturing defect detection method using association rule mining techniques
    Chen, WC
    Tseng, SS
    Wang, CY
    EXPERT SYSTEMS WITH APPLICATIONS, 2005, 29 (04) : 807 - 815
  • [40] Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application
    Archevapanich, Tuanjai
    Krungseanmuang, Woranidtha
    Chaowalittawin, Vasutorn
    Sathaporn, Posathip
    Chaowalittawin, Punyisa
    Purahong, Boonchana
    2024 6TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND THE INTERNET, ICCCI 2024, 2024, : 55 - 60