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.