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.

中文翻译：

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通过适当的数据增强来最小化卷积神经网络训练数据以进行内联缺陷分类

检测使用制造工艺生产的半导体器件的缺陷对于质量保证至关重要，它需要高分辨率扫描电子显微镜图像的获取和准确分类。但是，由于自动化难度大，分级过程成本高，效率也必须提高。为了提高分类精度并降低分类器的成本，这是传统技术的主要瓶颈，我们提出了一种基于 VGG16 架构的深度卷积神经网络 (CNN)，并对训练图像进行了适当的数据增强。减少训练图像是降低创建分类器成本的有效方法。但是，在这种情况下，分类精度不足，因为它根据训练图像的数量而有很大差异。对训练图像进行适当的数据增强是解决这一问题的有效方法。需要注意的是，不当的数据增强会降低分类精度。此外，我们设法为可能的缺陷类型找到了最佳的数据增强类型。