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Adaptation of neural network and application of digital ultrasonic image processing for the pattern recognition of defects in semiconductor

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4 Author(s)
Jae-Yeol Kim ; Div. of Mech. Eng., Chosun Univ., Kwangju, South Korea ; Hyun-Jo Jeong ; Hun-Cho Kim ; Chang-Hyun Kim

In this study, the classification of artificial defects in semiconductor devices are performed by using pattern recognition technology. For this target, a pattern recognition algorithm including user made software was developed and the total procedure including image processing and self-organizing map was treated by a backpropagation neural network, where image processing was composed of ultrasonic image acquisition, equalization filtering, binary processing and edge detection. Image processing and self-organizing map were compared as preprocessing methods for the reduction of dimensionality as input data into multi-layer perceptron or backpropagation neural networks. Also, the pattern recognition technique has been applied to classify two kinds of semiconductor defects: cracks and delamination. According to these results, it was found that the self-organizing map provided recognition rates of 83.4% and 75.7% for delamination and cracks, respectively, while BP provided 100% recognition rates for the results

Published in:

Electronic Materials and Packaging, 2001. EMAP 2001. Advances in

Date of Conference:

2001