Display and semiconductor manufacturing require inspection and repair process steps to increase the final product yield. To this end, it is necessary to divide into normal and defective images based on display and semiconductor images taken through an optical camera. This is a simple binary classification problem, but for the repair process, a more detailed classification technique is required. In order to automate this and solve it through deep learning, it is necessary to collect enough training data for each class. However, there are problems with certain defective classes that the deep learning model can't get enough to train. This greatly delays the time to apply the classification algorithm to the field, which adversely affects product mass production. In this paper, by using the deep learning method, sparse defective class images are naturally created, contributing to improving the performance of the final classification model. In addition, it is confirmed through experiments that artificially created images are made with the same shape and characteristics as non-made images of the same class.
In actual industrial sites, the ability of the deep learning model to detect defects at a high speed and reducing the time required to train the model is also a very important issue. In this paper, we propose a fast and accurate deep learning model and training method that can be applied to inspect the TFT-LCD(The Film Transistor - Liquid Crystal Display) PAD area image. The deep learning model we propose is a lightweight model based on U-net. By training only about 250,000 parameters, it was possible to confirm excellent performance in defect segmentation. In addition, a study on train data was also conducted so that the model can learn more effectively. We studied a method of training both normal images (images without defects) and abnormal images (images with defects), and it was confirmed that this performance showed better performance than when only data with defects were learned. It was shown that the method of learning both normal and abnormal results in a 50% or more reduction in the incidence of false judgment images than the method of learning only simple abnormal data.
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