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
  • [41] Visual defect detection methods overview
    Kraszewski, Tomasz
    Piwowar, Anna
    Fraczek, Renata
    Swiszcz, Piotr
    Bis, Lukasz
    PRZEGLAD ELEKTROTECHNICZNY, 2022, 98 (11): : 269 - 272
  • [42] A defect detection system for wire arc additive manufacturing using incremental learning
    Li, Yuxing
    Polden, Joseph
    Pan, Zengxi
    Cui, Junyi
    Xia, Chunyang
    He, Fengyang
    Mu, Haochen
    Li, Huijun
    Wang, Lei
    JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 2022, 27
  • [43] Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks
    Breitenbach, Johannes
    Eckert, Isabelle
    Mahal, Vanessa
    Baumgartl, Hermann
    Buettner, Ricardo
    PROCEEDINGS OF THE 55TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, 2022, : 1226 - 1235
  • [44] Error Detection using Augmented Reality in the Subtractive Manufacturing Process
    Sreekanta, Meghashree Hulkood
    Sarode, Abhishek
    George, Kiran
    2020 10TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2020, : 592 - 597
  • [45] How Particle Detector Can Aid Visual Inspection for Defect Detection of TFT-LCD Manufacturing
    Khakifirooz, Marzieh
    Fathi, Mahdi
    UBICOMP/ISWC '20 ADJUNCT: PROCEEDINGS OF THE 2020 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2020 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS, 2020, : 547 - 552
  • [46] Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing
    T. Herzog
    M. Brandt
    A. Trinchi
    A. Sola
    A. Molotnikov
    Journal of Intelligent Manufacturing, 2024, 35 : 1407 - 1437
  • [47] Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing
    Herzog, T.
    Brandt, M.
    Trinchi, A.
    Sola, A.
    Molotnikov, A.
    JOURNAL OF INTELLIGENT MANUFACTURING, 2024, 35 (04) : 1407 - 1437
  • [48] Deep Learning Applied to Defect Detection in Powder Spreading Process of Magnetic Material Additive Manufacturing
    Chen, Hsin-Yu
    Lin, Ching-Chih
    Horng, Ming-Huwi
    Chang, Lien-Kai
    Hsu, Jian-Han
    Chang, Tsung-Wei
    Hung, Jhih-Chen
    Lee, Rong-Mao
    Tsai, Mi-Ching
    MATERIALS, 2022, 15 (16)
  • [49] Overview on Machine Vision Based Surface Defect Detection and Quality Classification in the Leather Manufacturing Process
    Smith, A. D.
    Du, S.
    Kurien, A.
    ADVANCES IN VISUAL COMPUTING, ISVC 2022, PT II, 2022, 13599 : 344 - 356
  • [50] Anomaly Detection Model Based Visual Inspection Method for PCB Board Manufacturing Process
    Lee, Sang-Jeong
    Seo, Sung-Bal
    Bae, You-Suk
    Transactions of the Korean Institute of Electrical Engineers, 2024, 73 (11): : 2024 - 2029