首页> 外文会议>Conference on Metrology, Inspection, and Process Control for Semiconductor Manufacturing >Contour-based metrology for assessment of Edge Placement Error and its decomposition into global/local CD uniformity and LELE intralayer overlay
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Contour-based metrology for assessment of Edge Placement Error and its decomposition into global/local CD uniformity and LELE intralayer overlay

机译:基于轮廓的计量,用于评估边缘放置误差及其分解成全球/本地CD均匀性和leel intoralayer覆盖物

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Edge placement error (EPE) analysis, which combines pattern variation data from single litho-process steps with overlay data from subsequent litho-process steps, has been well established as a key methodology to characterize the performance of complex semiconductor manufacturing processes. As critical dimensions shrink in new semiconductor technologies, process margins become tighter, and characterizing and monitoring EPE budgets becomes more important than ever to assess and maintain in-line process performance and yield. In this paper, we present SEM image contour-based EPE analysis and budget generation for a BEOL multi-patterning (LELE) layer. SEM contour analysis was previously shown to be a suitable method for pattern variability characterization, with the capability to capture not only pattern size, but also shape and local stochastic placement variations, and to provide statistical overlay margin estimates between separate device layers. In the current work, we also show that for a LELE process, contour analysis provides local overlay measurements and all inputs needed to generate the complete EPE budget breakdown. Multiple wafers from a device in production were provided after processing the second etch step of a metal layer LELE process. We acquire large field-of-view SEM images with a high-throughput e-beam tool (HMI eP5), sampled within die, across exposure field and across wafer in order to enable analysis of variability into global and local components. Pattern contours are extracted from individual SEM images, and contours are 'stacked' to identify specific locations of largest variability or smallest margin. While the images contain patterns from both processing steps, these can be uniquely distinguished after die-to-database alignment and labeled by mask ID, here 1st and 2nd litho-etch layers, respectively. In addition to size, shape and stochastic placement variations, we perform center-of-gravity analysis between patterns on the 1st and 2nd litho-etch layers. The latter reveals local on-device overlay variations that can be mapped across the measured wafers. The contour analysis therefore provides all information required for a thorough EPE budget breakdown, i.e. global CDU and local CDU for the most critical cutline locations, as well as overlay. We perform EPE budget analysis for multiple wafers, which can highlight wafer-to-wafer variations. This is a first step toward process monitoring, which would not only highlight process drifts, but also distinguish main contributors in order to aid in trouble shooting.
机译:边缘放置误差(EPE)分析将图案变化数据与来自随后的Litho-Process步骤的覆盖数据相结合的来自单个Litho-Process步骤,已经很好地建立为表征复杂半导体制造过程的性能的关键方法。由于新的半导体技术中的临界尺寸缩小,过程边距变得更加紧张,并且表征和监测EPE预算比以往更重要,以评估和维持在线流程性能和产量。在本文中,我们为BEOL多图案化(LELE)层提供了基于SEM图像轮廓的EPE分析和预算生成。 SEM轮廓分析先前被证明是用于模式可变性表征的合适方法,其不仅能够捕获图案尺寸,而且捕获图案和局部随机放置变化,并在单独的设备层之间提供统计覆盖边缘估计。在当前的工作中,我们还表明,对于LELE过程,轮廓分析提供了本地叠加测量和生成完整EPE预算崩溃所需的所有输入。在处理金属层leele工艺的第二蚀刻步骤之后提供了来自生产中的装置的多个晶片。我们使用高吞吐量电子束工具(HMI EP5)获取大型视野图像,在模具中采样,横跨曝光场和晶圆,以便能够分析为全局和本地组件的可变性。模式轮廓从单独的SEM图像中提取,并且轮廓“堆叠”以识别最大可变性或最小余量的特定位置。虽然图像包含来自处理步骤的图案,但是在芯片到数据库对齐之后可以唯一地区分,并且分别由掩模ID标记为第一和第二光刻蚀刻层。除大小,形状和随机放置变化外,我们还在第1和第2个蚀刻层上的图案之间进行重心分析。后者揭示了可以在测量的晶片上映射的本地设备上的覆盖变型。因此,轮廓分析提供了彻底的EPE预算崩溃所需的所有信息,即全局CDU和最关键的切割位置的本地CDU,以及覆盖层。我们对多个晶片进行EPE预算分析,可以突出晶片到晶圆变化。这是迈向过程监控的第一步,这不仅突出了过程漂移,还可以区分主要贡献者,以帮助攻击故障。

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