Abstract
Thin film transistor (TFT) lines on glass substrates of flat panel displays (FPD) often contain many electrical defects such as open circuits and short circuits that have to be inspected and detected in early manufacturing stages in order to repair and restore them. This paper proposes a multiobjective evolutionary optimized recurrent neural network for inspection of such electrical defects. The inspection is performed on digitized waveform data of voltage signals that are captured by a capacitor based non-contact sensor through scanning over TFT lines on the surface of mother glass of FPD. Waveform data that were captured over TFT lines, which contain open or short circuits, show irregular patterns and the proposed RNN is capable of classifying and detecting them. A multiobjective evolutionary optimization process is employed to determine the parameters of the best suited topology of the RNN. This method is an extension to address the drawbacks in our previous work, which utilizes a feed-forward neural network. Experimental results show that this method is capable of detecting defects on more realistic and noisy data than both of the previous method and the conventional threshold based method.
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Abeysundara, H.A., Hamori, H., Matsui, T., Sakawa, M. (2014). A Multiobjective Evolutionary Optimized Recurrent Neural Network for Defects Detection on Flat Panel Displays. In: Torra, V., Narukawa, Y., Endo, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2014. Lecture Notes in Computer Science(), vol 8825. Springer, Cham. https://doi.org/10.1007/978-3-319-12054-6_15
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DOI: https://doi.org/10.1007/978-3-319-12054-6_15
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