Ang, J.H., Goh, C.K., Teoh, E.J. and Mamum, A.A. (2007) Multi-Objective Evolutionary Recurrent Neural Network for System Identification. Proceedings of IEEE Congress on Evolutionary Computations, Singapore, 25-28 September 2007, 1586-1592.

has been cited by the following article:

• TITLE: Defects Detection of TFT Lines of Flat Panel Displays Using an Evolutionary Optimized Recurrent Neural Network

  AUTHORS: Hapu Arachchilage Abeysundara, Hiroshi Hamori, Takeshi Matsui, Masatoshi Sakawa

  KEYWORDS: Non-Contact Defects Inspection, Recurrent Neural Networks, Evolutionary Optimization, Open Short Detection

  JOURNAL NAME: American Journal of Operations Research, Vol.4 No.3, April 30, 2014

  ABSTRACT: This paper proposes an evolutionary optimized recurrent neural network for inspection of open/short defects on thin film transistor (TFT) lines of flat panel displays (FPD). 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. Irregular patterns on the waveform, sudden deep falls (open circuits) or sharp rises (short circuits), are classified and detected by employing the optimized recurrent neural network. The topology parameters of the recurrent neural network are optimized by a multiobjective evolutionary optimization process using a selected training data set. This method is an extension to our previous work, which utilized a feed-forward neural network, to address the drawbacks in it. Experimental results show that this method can detect defects on more realistic and noisy data than both of the previous method and the conventional threshold based method.