Paper
9 December 2015 A real-time marking defect inspection method for IC chips
Author Affiliations +
Proceedings Volume 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015); 98170S (2015) https://doi.org/10.1117/12.2228808
Event: Seventh International Conference on Graphic and Image Processing, 2015, Singapore, Singapore
Abstract
IC marking provides information about the integrated circuit chips, such as product function and classification. So IC marking inspection is one of the essential processes in semiconductor fabrication. A real-time IC chip marking defect inspection method is presented in this paper. The method comprises the following steps: chip position detection, characters segmentation, feature extraction and classification. The extracted features are used in a back propagation neural network for classifying the types of marking errors such as illegible characters, missing characters and misprinted characters. Character segmentation is an essential part of the inspection method. It is a considerable challenge to segment touching and broken characters correctly, due to uneven illumination, motion blur, as well as problems in the printing process. In order to segment the characters rapidly and accurately, a novel approach for character segmentation based on vertical projection and the character features is proposed. Experiments using a TSSOP20 packaging chip demonstrate that our method can inspect an IC marking with 17 different characters in just 130ms. The system achieves a maximum recognition rate of 98.5%. As a result, it is an ideal solution for a real-time IC marking recognition and defects inspection system.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hua Yang, Buyang Zhang, and Yang Hu "A real-time marking defect inspection method for IC chips", Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98170S (9 December 2015); https://doi.org/10.1117/12.2228808
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Cited by 2 scholarly publications.
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KEYWORDS
Image segmentation

Inspection

Feature extraction

Defect inspection

Image processing

Neural networks

Optical character recognition

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