Deep learning technique for generating sparse defects to improve image classification performance

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.