27 March 1995 Vision-based defect detection method for fine-pitch surface-mounted devices
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Abstract
The inspection of fine pitch surface-mounted devices by comparison of defect-free and defective packages is a promising area of research. The types of defects considered include missing pins, bent pins, broken pins, and bad solder connections on mounted packages. The detection algorithm includes morphological image processing operations followed by a neural network. The feature extraction steps include morphological filtering for thresholding, skeletonization, and centroid determination. The centroids are used as inputs to a backpropagation neural network for determining the presence of defects. The neural network compares the input data against data representing defect-free packages and produces a measure of how closely the two data sets match. The accuracy of the network in identifying both good and defective packages is discussed with output values interpreted both incorporating and not incorporating rejection on the bases of a minimal threshold for output values and a minimal separation between output values. The algorithm performance is evaluated based on its performance in correctly identifying the presence or absence of a number of frequently found defect types. Evaluation is also based on the neural network performance with different training parameters. The neural network is shown to identify defects over 70% of the time without rejection and often 100% of the time with rejection.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arthur J. Na, Dongming Zhao, Malayappan Shridhar, "Vision-based defect detection method for fine-pitch surface-mounted devices", Proc. SPIE 2423, Machine Vision Applications in Industrial Inspection III, (27 March 1995); doi: 10.1117/12.205496; https://doi.org/10.1117/12.205496
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KEYWORDS
Neural networks

Image processing

Inspection

Defect detection

Image segmentation

Defect inspection

Pattern recognition

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