Minimizing Convolutional Neural Network Training Data With Proper Data Augmentation for Inline Defect Classification

Abstract

Detecting the defects of the semiconductor devices produced using manufacturing processes is essential for quality assurance, and it requires the acquisition and accurate classification of high-resolution scanning electron microscopy images. However, owing to the difficulty of automation, the classification process is costly, and its efficiency must also be improved. To improve the classification accuracy and reduce the cost of classifiers, which are the main bottlenecks of conventional technology, we proposed a deep convolutional neural network (CNN) based on the VGG16 architecture and performed appropriate data augmentations on training images. Reducing training images is an effective method for reducing the cost of creating classifiers. However, in this case, the classification accuracy is insufficient, as it greatly varies depending on the number of training images. The appropriate data augmentation of training images is an effective method for solving this problem. It was important to note that improper data augmentation reduces the classification accuracy. Also, we managed to find the optimal data augmentation type for a possible defect type.