Defects detection on TFT lines of flat panels using a feed forward neural network

This paper proposes a novel approach 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 by scanning over TFT lines on the surface of mother glass of FPD. The sudden deep falls (open circuits) or sharp rises (short circuits) on the captured noisy waveform are classified and detected by employing a four-layer feed forward neural network with two hidden layers. The topology of the network consist of an input layer with two units, two hidden layers with two and three units respectively and an output layer with one unit and a standard sigmoid function as the activation function of each unit. The network is trained with a fast adaptive back-propagation algorithm to find an optimal set of associated weights of neurons by feeding a known set of input data. This method is an alternative to the existing thresholding based non-contact method which has always its own limitations and drawbacks due to some non-avoidable features of input data such as non-stationary patterns and varying magnitude levels at defect points. Experimental results show that this method can adapt fast for new input patterns and avoids the ambiguity of threshold definitions and therefore it is more feasible than the existing thresholding method.