CNNs Combined With a Conditional GAN for Mura Defect Classification in TFT-LCDs

Abstract

Mura defect classification is a critical concern for thin-film transistor liquid crystal display (TFT-LCD) manufacturers. In recent years, artificial intelligence technologies have been successfully applied in numerous areas. However, such approaches require large amounts of training image data. Simultaneously, product differentiation and customization strategies have forced the TFT-LCD manufacturing industry to shift from mass production to high-mix, low-volume, and short-life-cycle production. In this environment, collecting a large amount of training data is difficult. Moreover, images with Mura defects captured at inspection stations remain challenging because they are often contaminated with moiré patterns. Moiré patterns severely affect the visual quality of images and cause difficulty in determining Mura defects. This study proposes an approach to eliminate moiré patterns from defect images using a conditional generative adversarial network. In addition, we develop a transfer learning ensemble model that aggregates multiple convolutional neural networks based on a denoising network for defect classification in a limited training data set. The results from an industrial case study demonstrate that the proposed method provides improved accuracy for Mura defect classification. This method can therefore become a viable alternative to manual classification in the TFT-LCD manufacturing industry.