S. Halder, D. Cerbu, M. Saib, P. Leray
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引用次数: 3

摘要

随着技术节点的不断缩小,光刻技术的挑战也越来越大。在20nm节点,双图案技术(DPT)是实现精细器件结构的常用方法。对于14nm以下的节点,IDM的模式选择是SAQP + EUV块或基于EUV的直接模式方法。与采用任何新技术一样,一开始的产量上升需要付出努力。由光学检查工具识别的缺陷位置需要由检查扫描电镜进行检查,以准确地了解在光学工具标记的区域中哪个特征是失败的。通过review-SEM工具抓取的图像用于分类,但很少用于量化。本文的目的是看看现有的数千张评论扫描电镜图像是否可以用于量化和进一步分析。更具体地说,我们使用连接分量和K-means聚类分析算法来解决SEM量化问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review-SEM image analysis with K-means algorithm: AM: Advanced metrology/DI: Defect inspection
With the continuous shrink of technology nodes, lithography becomes more and more challenging. At 20 nm node, double patterning technology (DPT) was the usual way of achieving the fine device structures. For sub-14nm nodes the patterning choices for IDM's lie between SAQP plus EUV block or an EUV based direct patterning approach. As with any new technology adoption, yield ramp at the beginning takes effort. Defect locations identified by optical inspection tools need to be reviewed by review-SEM's to understand exactly which feature is failing in the region flagged by the optical tool. The images grabbed by the review­SEM tool are used for classification but rarely for quantification. The goal of this paper is to see if the thousands of existing review-SEM images can be used for quantification and further analysis. More specifically we address the SEM quantification problem with connected component and K-means cluster analysis algorithms.
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