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In-Cell Overlay Metrology by Using Optical Metrology Tool

机译:通过使用光学计量工具,细胞内覆盖计量

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Overlay is one of the most critical process control steps of semiconductor manufacturing technology. A typical advanced scheme includes an overlay feedback loop based on after litho optical imaging overlay metrology on scribe-. line targets. The after litho control loop typically involves high frequency sampling: every lot or nearly every lot. An after etch overlay metrology step is often included, at a lower sampling frequency, in order to characterize and compensate for bias. The after etch metrology step often involves CD-SEM metrology, in this case in-cell and on-device. This work explores an alternative approach using spectroscopic ellipsometry (SE) metrology and a machine learning analysis technique. Advanced 1× nm DRAM wafers were prepared, including both nominal (POR) wafers with mean overlay offsets, as well as DOE wafers with intentional across wafer overlay modulation. After litho metrology was measured using optical imaging metrology, as well as after etch metrology using both SE and CD-SEM for comparison. We investigate 2 types of machine learning techniques with SE data: model-less and model-based, showing excellent performance for after etch in-cell on-device overlay metrology.
机译:覆盖层是半导体制造技术最关键的过程控制步骤之一。典型的高级方案包括基于Litho光学成像覆盖计量在划线上的覆盖反馈回路。线目标。 Litho控制回路通常涉及高频采样:每批或几乎每一批。在较低的采样频率下通常包括蚀刻覆盖计量步骤,以表征和补偿偏差。在这种情况下,蚀刻计量步骤通常涉及CD-SEM Metrology,在这种情况下和设备上。该工作探讨了使用光谱椭圆形测量(SE)计量和机器学习分析技术的替代方法。准备了先进的1×NM DRAM晶片,包括具有平均覆盖偏移的标称(POR)晶片,以及DOE晶片,具有故意跨晶片覆盖调制。使用光学成像计量测量Litho Metrology之后,以及使用SE和CD-SEM的蚀刻计量以进行比较。我们调查2种类型的机器学习技术,具有SE数据:较少的模型和基于模型,显示出蚀刻内 - 设备内覆盖计量计量后的出色性能。

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