基于稀疏度的SEM图像超分辨率,Nano Letters

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基于稀疏度的SEM图像超分辨率
Nano Letters ( IF 9.6 ) Pub Date : 2017-08-29 00:00:00 , DOI: 10.1021/acs.nanolett.7b02091
Shahar Tsiper , Or Dicker , Idan Kaizerman ₁ , Zeev Zohar ₁ , Mordechai Segev , Yonina C. Eldar

Affiliation

扫描电子显微镜（SEM）是一种电子显微镜，通过用聚焦的电子束扫描样品来产生图像。电子与样品中的原子相互作用，从而发射包含表面形貌和成分信息的二次电子。电子束逐点扫描样品，直到形成表面图像。自1942年发明以来，SEM的功能已成为发现和理解纳米世界的重中之重，如今它已广泛用于研究和工业领域。原则上，SEM可以实现优于一纳米的分辨率。但是，对于许多应用程序而言，以亚纳米分辨率工作意味着扫描点数量过多。正是由于这个原因，微电子芯片的SEM诊断可以在较小的区域内以高分辨率（HR）进行，也可以以低分辨率（LR）进行，同时捕获更大的芯片部分。在这里，我们采用稀疏编码和字典学习来算法上增强微电子芯片的低分辨率SEM图像-达到通过慢速SEM扫描获得的HR图像的水平，同时显着降低了噪声。我们的方法包括两个步骤：从芯片中同一区域的LR和HR图像序列中学习联合词典的离线阶段，然后是快速在线超分辨率步骤，其中新LR图像的分辨率为增强。我们提供了微电子工业中使用的典型芯片的几个示例，以及具有特征结构特征的任意图像的统计研究。从概念上讲当图像具有相似的特征时，我们的方法可以很好地工作，就像微电子芯片一样。这项工作表明，采用稀疏性概念可以极大地提高SEM的性能，从而在不影响分析质量和分辨率的情况下大大提高扫描吞吐量。

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Sparsity-Based Super Resolution for SEM Images

The scanning electron microscope (SEM) is an electron microscope that produces an image of a sample by scanning it with a focused beam of electrons. The electrons interact with the atoms in the sample, which emit secondary electrons that contain information about the surface topography and composition. The sample is scanned by the electron beam point by point, until an image of the surface is formed. Since its invention in 1942, the capabilities of SEMs have become paramount in the discovery and understanding of the nanometer world, and today it is extensively used for both research and in industry. In principle, SEMs can achieve resolution better than one nanometer. However, for many applications, working at subnanometer resolution implies an exceedingly large number of scanning points. For exactly this reason, the SEM diagnostics of microelectronic chips is performed either at high resolution (HR) over a small area or at low resolution (LR) while capturing a larger portion of the chip. Here, we employ sparse coding and dictionary learning to algorithmically enhance low-resolution SEM images of microelectronic chips—up to the level of the HR images acquired by slow SEM scans, while considerably reducing the noise. Our methodology consists of two steps: an offline stage of learning a joint dictionary from a sequence of LR and HR images of the same region in the chip, followed by a fast-online super-resolution step where the resolution of a new LR image is enhanced. We provide several examples with typical chips used in the microelectronics industry, as well as a statistical study on arbitrary images with characteristic structural features. Conceptually, our method works well when the images have similar characteristics, as microelectronics chips do. This work demonstrates that employing sparsity concepts can greatly improve the performance of SEM, thereby considerably increasing the scanning throughput without compromising on analysis quality and resolution.

更新日期：2017-08-30

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