一种有效的基于DCNN的扫描电镜图像轮廓提取方法

Tao Zhou, X. Shi, Yanyan, Chen Li, Shoumian Chen, Yuhang Zhao, Wenzhan Zhou, Kan Zhou, Xuan Zeng
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引用次数: 5

摘要

扫描电镜图像轮廓提供了关于图案质量和能力的有价值的信息。从扫描电镜图像轮廓中提取或估计出临界尺寸和抗蚀侧壁角等几何特性。这些几何特性可用于OPC模型标定、OPC模型验证和光刻热点检测。提出了一种基于机器学习的扫描电镜图像轮廓提取方法。将设计好的DCNN网络与自制的高质量数据集相结合,进行轮廓模型训练。基于较高的图像/特征表示能力和显著的硬件加速并行计算优势,该模型实现了轮廓提取的高精度和实时性,更重要的是,它提供了区分和分离SEM图像上下轮廓的能力。此外,该模型不仅消除了大量的边缘,而且还修复了由于工艺和测量技术不完善而造成的局部不连续。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An effective method of contour extraction for SEM image based on DCNN
SEM-image contours provide valuable information about patterning quality and capability. Geometrical properties such as critical dimension and resist sidewall angle could be extracted or estimated from SEM image contours. Those geometrical properties can be used for OPC model calibration, OPC model verification and lithography hotspot detection. This work presents a machine learning based method for contour extraction of SEM image. A designed DCNN network and self-made high quality dataset are combined for contour model training. Based on the high capability of image/feature representation and remarkable advantage of parallel computing with hardware acceleration, the model achieves high accuracy and real-time operation for contour extraction, more importantly, it provides the ability to distinguish and separate the top and bottom contours of SEM images. Additionally, the model not only removes the abundant edges but also repairs the local discontinuity caused by imperfect process and measuring technique.
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