In‐line mura detection using machine learning and subspace method in display manufacturing

Abstract This paper discusses the automatic detection of mura, non‐uniformity of brightness or color, which has been a long‐standing challenge in the display industries. Our purpose is to develop a method using machine learning, which automatically detects and classifies mura in the front‐end process. This will enable prompt feedback to the manufacturing process and contribute to improvement of the productivity. We propose “Progressive Hybrid model,” which is based on the human visual perception and consists of a multiclass CNN (Convolutional Neural Network), a 2‐class residual neural network, and a 2‐class CNN. The two 2‐class models based on the subspace method to reproduce the boundary‐samples in the human visible test are for accurate classification between Normal displays and weak mura. To reproduce the appropriate test‐images used in the human visible test, we enhance contrast of 8000 images of the dataset obtained from an OLED (Organic Light Emitting Diode) manufacturing line. The evaluation results show improvement of the accuracy from 0.830 in the general application of CNN to 0.884 in the proposed model. The proposed model is particularly effective to improve classification of Normal displays, which is usually the most numerous in the manufacturing, and thus further beneficial in the practical use.