Machine Learning Virtual SEM Metrology

Yan Yan, X. Shi, Tao Zhou, Bowen Xu, Chen Li, Yifei Lu, Ying Gao
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引用次数: 1

Abstract

E-beam metrology, both CDSEM metrology and defect scan metrology, have been playing a very critical role in assessing post lithography or post etch patterning quality. SEM images can provide rich visual information for engineers to do qualitative and quantitative analysis. However, the lowe-beam metrology tool throughput makes it impossible to obtain SEM images for very large area. Monte Carlo based SEM image simulations are slow and they also require post lithography or post etch pattern 3D structures as prerequisite. To bridge the gap, we have proposed a Virtual SEM Metrology solution using physics based feature maps and the U-net neural network. With information in aerial image space encoded properly, SEM images of both post lithography and post etch can be predicted accurately enough for practical applications using our proposed Virtual SEM Metrology models.
机器学习虚拟SEM计量
电子束测量,包括CDSEM测量和缺陷扫描测量,在评估光刻后或蚀刻后图案质量方面起着非常重要的作用。扫描电镜图像可以为工程师进行定性和定量分析提供丰富的视觉信息。然而,低光束计量工具的吞吐量使得无法获得非常大区域的扫描电镜图像。基于蒙特卡罗的SEM图像模拟速度较慢,并且还需要后光刻或后蚀刻模式3D结构作为先决条件。为了弥补这一差距,我们提出了一个使用基于物理的特征图和U-net神经网络的虚拟SEM计量解决方案。通过对航空图像空间中的信息进行适当编码,可以使用我们提出的虚拟扫描电镜计量模型对光刻后和蚀刻后的扫描电镜图像进行足够准确的预测,以供实际应用。
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