Detection Method of CNN-Based Classification for Conductive Particles in TFT-LCD Manufacturing

The conductivity is determined by the number of conductive particles attached to Thin Film Transistor Liquid Crystal Display (TFT-LCD) bumps. The image detection method for the number of particles is widely used to judge the quality of TFT-LCD bumps. We propose a detection method of CNN-based Classification for conductive particles in TFT-LCD manufacturing. First, aiming at the phenomenon that the adhesion of conductive particles seriously affects the detection accuracy, a binary classification detection of bumps conductivity is proposed based on a static number of particles attached to the bumps. Second, a convolutional neural network (CNN) classification model is established; due to the bump images with multiple gray levels, principal component analysis (PCA) feature selection is introduced into the classification model. This model can learn features of particle from massively labeled data. The experimental results show that the accuracy of our proposed method is 95.55%. The accuracy is 1.46% higher than the K-means clustering + CNN method, 2.45% higher than the watershed method, 3.00% higher than the K-means clustering method and 7.70% higher than the Otsu method. The method proposed in this paper can be effectively applied to the quality detection of TFT-LCD bump.