利用缺陷自动分类技术提高缺陷尺寸测量的精度

Bhamidipati Samir, Mark Pereira, Sankaranarayanan Paninjath, Chan-uk Jeon, Dong-Hoon Chung, Gi-sung Yoon, H. Jung
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引用次数: 3

摘要

空白掩模缺陷审查过程包括对基材多个制备阶段观察到的缺陷进行详细分析,例如清洁和电阻涂层。对这些缺陷的详细了解对使用毛坯获得的最终良率起着重要的作用。缺陷知识主要由细节组成,例如观察到的缺陷数量,以及它们的精确大小。遮罩可用性评估在准备过程的开始,是基于缺陷的数量。同样,缺陷尺寸也反映了晶圆缺陷最终的可印刷性。此外,监测缺陷特征,特别是尺寸和形状,有助于获得与工艺相关的信息,如清洁或涂层工艺效率。空白掩模缺陷评审过程基本上是手工的。然而,对于减小半节距尺寸的最新技术节点,观察到大量缺陷;以及相关的信息量,一起使得这个过程在审查时间、准确性和一致性方面变得越来越低效。可能需要使用其他工具,如CDSEM,以进一步帮助审查过程,从而增加成本。Calibre®mdpautoclassification™提供了一种自动化的软件替代方案,以强大的分析工具的形式,用于快速,准确,一致和自动分类空白缺陷。精细的后处理算法应用于检测机器生成的缺陷图像,提取和报告重要的缺陷信息,如缺陷大小,影响缺陷可打印性和掩模可用性。在缺陷的性质、大小、形状和组成方面,所遇到的缺陷的多样性和复杂性对算法的能力提出了挑战;以及缺陷周围发生的光学现象[1]。本文主要关注Calibre®mdpautoclassified™产品的评估结果。这种评估的主要目的是评估从检测图像中自动准确估计缺陷大小的能力。对微弱缺陷信号的敏感性、滤除噪声识别缺陷信号以及在图像中定位缺陷是成功的关键因素。该工具的性能在可编程缺陷掩码和来自HVM生产流程的生产掩码上进行了评估。与检测机报告的缺陷尺寸相比,Calibre®mdpautoclassified™的实施预计将提高缺陷尺寸的准确性,这对生产非常关键,缺陷分类将有助于达到适当的处置,如SEM审查,修复和报废。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improvement in accuracy of defect size measurement by automatic defect classification
The blank mask defect review process involves detailed analysis of defects observed across a substrate’s multiple preparation stages, such as cleaning and resist-coating. The detailed knowledge of these defects plays an important role in the eventual yield obtained by using the blank. Defect knowledge predominantly comprises of details such as the number of defects observed, and their accurate sizes. Mask usability assessment at the start of the preparation process, is crudely based on number of defects. Similarly, defect size gives an idea of eventual wafer defect printability. Furthermore, monitoring defect characteristics, specifically size and shape, aids in obtaining process related information such as cleaning or coating process efficiencies. Blank mask defect review process is largely manual in nature. However, the large number of defects, observed for latest technology nodes with reducing half-pitch sizes; and the associated amount of information, together make the process increasingly inefficient in terms of review time, accuracy and consistency. The usage of additional tools such as CDSEM may be required to further aid the review process resulting in increasing costs. Calibre® MDPAutoClassify™ provides an automated software alternative, in the form of a powerful analysis tool for fast, accurate, consistent and automatic classification of blank defects. Elaborate post-processing algorithms are applied on defect images generated by inspection machines, to extract and report significant defect information such as defect size, affecting defect printability and mask usability. The algorithm’s capabilities are challenged by the variety and complexity of defects encountered, in terms of defect nature, size, shape and composition; and the optical phenomena occurring around the defect [1]. This paper mainly focuses on the results from the evaluation of Calibre® MDPAutoClassify™ product. The main objective of this evaluation is to assess the capability of accurately estimating the size of the defect from the inspection images automatically. The sensitivity to weak defect signals, filtering out noise to identify the defect signals and locating the defect in the images are key success factors. The performance of the tool is assessed on programmable defect masks and production masks from HVM production flow. Implementation of Calibre® MDPAutoClassify™ is projected to improve the accuracy of defect size as compared to what is reported by inspection machine, which is very critical for production, and the classification of defects will aid in arriving at appropriate dispositions like SEM review, repair and scrap.
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