使用Calibre晶圆缺陷工程和机器学习解决方案减少系统缺陷

Jet Jiang, Frank Hou, Gavin Li, Summy Chen, Marfei Fei, Qian Xie, Liang Cao, Qijian Wan, Xinyi Hu, Chunshan Du, David Wang, Elven Huang, Sankaranarayanan Paninjath, S. Madhusudhan, Leo Tian
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引用次数: 4

摘要

随着半导体制造不断向更先进的技术节点迈进,设计和工艺引入的系统性缺陷成为重要的良率限制因素[1]。因此,识别和描述这些系统缺陷变得越来越重要。设计系统缺陷分析通常是通过结合在线检查结果和物理布局(设计)信息来完成的。在整个流程中,从准备检查护理区到进行系统的缺陷根本原因分析,利用EDA软件起着重要的作用。特别是结合OPC特征向量提取的机器学习技术,可以对晶圆上的弱模式进行更精确的分析。在本文中,我们将介绍我们如何利用这些技术进行工艺窗口鉴定(PWQ),主要关注我们如何执行BFI到SEM下采样,以及全芯片热点预测来验证PWQ晶圆上的潜在热点,以获得准确的工艺窗口,并识别系统的弱模式,增加SEM缺陷发生率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reducing Systematic Defects using Calibre Wafer Defect Engineering and Machine Learning Solutions
As the semiconductor manufacturing continues its march towards more advanced technology nodes, design and process introduced systematic defects become significant yield limiters [1]. Therefore, identification and characterization of these systematic defects becomes increasingly important. The design systematic defect analysis is normally done by combining both inline inspection results and physical layout (design) information. In the full flow, from preparing inspection care area to performing systematic defect root cause analysis, utilizing EDA software plays an important role. Especially with machine learning technique combining with OPC feature vector extraction, we can have a more precise analysis of the weak pattern on wafers. In this paper, we will introduce how we utilize these techniques for Process Window Qualification (PWQ), focus mainly on how we perform BFI to SEM down sampling, and full chip hotspot prediction to verify potential hotspots on PWQ wafer in order to obtain an accurate process window, and identify systematic weak patterns with increased SEM defect hit rate.
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