论文标题
将卷积神经网络应用于TSOM图像，用于分类6 nm节点图案缺陷
Application of Convolutional Neural Network to TSOM Images for Classification of 6 nm Node Patterned Defects
论文作者
论文摘要
随着半导体行业的快速增长，检测和对越来越小的图案缺陷进行分类变得至关重要。最近，包括深度学习在内的机器学习已经大大帮助这项工作。但是，文献表明，使用低成本和高体积制造兼容的光学成像方法将成功在6 nm节点上成功分类的缺陷类型具有挑战性。在这里，我们将卷积神经网络（CNN）与称为贯穿对方扫描光学显微镜（TSOM）的光学成像方法的卷积神经网络（CNN）结合在一起，以使用193 nm照明波长的模拟光学图像成功地为6 nm节点目标分类了模式的缺陷。我们证明了缺陷的八种变化的成功分类，包括一个维度的缺陷大小的3 nm差异，比所用的照明波长小50倍以上。
With the rapid growth in the semiconductor industry, it is becoming critical to detect and classify increasingly smaller patterned defects. Recently machine learning, including deep learning, has come to aid in this endeavor in a big way. However, the literature shows that it is challenging to successfully classify defect types at the 6 nm node with 100% accuracy using low-cost and high-volume-manufacturing compatible optical imaging methods. Here we combine a convolutional neural network (CNN) with that of an optical imaging method called through-focus scanning optical microscopy (TSOM) to successfully classify patterned defects for the 6 nm node targets using simulated optical images at the 193 nm illumination wavelength. We demonstrate the successful classification of eight variations of the defects, including the 3 nm difference in the defect size in one dimension, which is over 50 times smaller than the illumination wavelength used.
