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OPC Model Accuracy Enhancement for Asymmetrical Layouts by Incorporating 2D Contour Extraction Methodology

机译:通过结合2D轮廓提取方法提高OPC模型不对称布局的精度

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As the semiconductor device dimension continues to shrink, the tolerance of Critical Dimension (CD) variation has been dramatically reduced for whole chip area to ensure the good uniformity of device functionality. Accordingly, the Optical Proximity Correction (OPC) model accuracy has become extremely important for controlling across-chip CD variation. The traditional method relying on CD Scanning Electron Microscope (CD-SEM) measurement data from synthetic patterns, such as dense line/space and isolated line in various pitch biases, for OPC model calibration is no longer sufficient for accurately projecting the complex patterns such as asymmetrical layout environments. Alternatively, the 2D extracted contours from SEM images associated with user-specified cutline algorithm to get the dimensional information of complex patterns have been incorporated for OPC modeling as well as process qualification [1-3]. In this work, the 2D contour extraction methodology was incorporated to extract more comprehensive CD information of complex features for OPC model calibration of 39nm half-pitch process layer. Together with the use of CD-SEM based CD, the model calibration via MASK2D and MASK3D model forms were conducted for accuracy comparison. The OPC model validation results demonstrated the contour-based gauges helps to improve the OPC model projection accuracy by 40-60% for asymmetrical features comparing to the traditional OPC calibration method.
机译:随着半导体器件尺寸的不断缩小,整个芯片面积的临界尺寸(CD)变异公差已大大降低,以确保器件功能的良好一致性。因此,光学邻近校正(OPC)模型的准确性对于控制跨芯片CD的变化变得极为重要。传统方法依赖于CD扫描电子显微镜(CD-SEM)的测量数据,这些数据来自合成图案,例如各种螺距偏置下的密集线/间距和孤立线,用于OPC模型校准已不足以准确投影复杂的图案,例如不对称的布局环境。另外,从SEM图像中提取的2D轮廓与用户指定的割线算法相关联,以获得复杂图案的尺寸信息,已用于OPC建模和工艺鉴定[1-3]。在这项工作中,采用了二维轮廓提取方法来提取复杂特征的更全面的CD信息,以用于39nm半间距工艺层的OPC模型校准。结合使用基于CD-SEM的CD,通过MASK2D和MASK3D模型表格进行了模型校准,以进行准确性比较。 OPC模型验证结果表明,与传统的OPC校准方法相比,基于轮廓的量规有助于将OPC模型的非对称特征的投影精度提高40-60%。

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