A Real-World Metal-Layer SEM Image Dataset with Partial Labels

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This dataset contains scanning electron microscope (SEM) images and labels from our paper "Towards Unsupervised SEM Image Segmentation for IC Layout Extraction", which are licensed under a Creative Commons Attribution 4.0 International License (CC-BY 4.0).
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The SEM images cover the logic area of the metal-1 (M1) and metal-2 (M2) layers of a commercial IC produced on a 128 nm technology node. We used an electron energy of 15 keV with a backscattered electron detector and a dwell time of 3 μs for SEM capture. The images are 4096×3536 pixels in size, with a resolution of 14.65 nm per pixel and 10% overlap. We discarded images on the logic area boundaries and publish the remaining ones in random order.
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We additionally provide labels for tracks and vias on the M2 layer, which are included as <code>.svg</code> files. For labeling, we employed automatic techniques, such as thresholding, edge detection, and size, position, and complexity filtering, before manually validating and correcting the generated labels. The labels may contain duplicates for detected vias. Tracks spanning multiple images may not be present in the label file of each image.
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The implementation of our approach, as well as accompanying evaluation and utility routines can be found in the following GitHub repository: <a href="https://github.com/emsec/unsupervised-ic-sem-segmentation" target="_blank">https://github.com/emsec/unsupervised-ic-sem-segmentation</a>
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Please make sure to always cite <a href="https://doi.org/10.1145/3605769.3624000" target="_blank">our study</a> when using any part of our data set or code for your own research publications!
</p><p><blockquote><pre>
@inproceedings {2023rothaug,
author = {Rothaug, Nils and Klix, Simon and Auth, Nicole and B\"ocker, Sinan and Puschner, Endres and Becker, Steffen and Paar, Christof},
title = {Towards Unsupervised SEM Image Segmentation for IC Layout Extraction},
booktitle = {Proceedings of the 2023 Workshop on Attacks and Solutions in Hardware Security},
series = {ASHES'23},
year = {2023},
month = {november},
keywords = {ic-layout-extraction;sem-image-segmentation;unsupervised-deep-learning;open-source-dataset},
url = {https://doi.org/10.1145/3605769.3624000},
doi = {10.1145/3605769.3624000},
isbn = {9798400702624},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA}
}
</pre></blockquote></p>

本数据集包含来自我们发表于论文《面向集成电路布局提取的无监督扫描电子显微镜图像分割》的扫描电子显微镜（Scanning Electron Microscope, SEM）图像与标注集，本数据集采用知识共享署名4.0国际许可协议（CC-BY 4.0）进行授权。
本数据集的SEM图像覆盖了采用128纳米工艺制程节点制造的商用集成电路（Integrated Circuit, IC）的金属-1（M1）与金属-2（M2）层的逻辑区域。本次SEM成像采用15 keV的电子束能量，搭配背散射电子探测器，扫描驻留时间为3 μs。所有图像尺寸均为4096×3536像素，分辨率为每像素14.65 nm，图像间重叠率为10%。我们剔除了逻辑区域边界处的图像，并将剩余图像以随机顺序发布。
我们额外提供了M2层走线与过孔的标注集，以可缩放矢量图形（Scalable Vector Graphics, .svg）文件格式存储。标注生成过程中，我们先采用阈值化、边缘检测以及尺寸、位置与复杂度过滤等自动化技术，再对生成的标注进行手动验证与修正。标注集中可能存在检测到的过孔重复条目。跨多张图像的走线可能不会出现在单张图像对应的标注文件中。
本研究方法的实现代码，以及配套的评估与实用工具程序均可在以下GitHub仓库中获取：<a href="https://github.com/emsec/unsupervised-ic-sem-segmentation" target="_blank">https://github.com/emsec/unsupervised-ic-sem-segmentation</a>
若您将本数据集或代码的任意部分用于学术研究发表，请务必引用我们的研究成果<a href="https://doi.org/10.1145/3605769.3624000" target="_blank">https://doi.org/10.1145/3605769.3624000</a>。
@inproceedings {2023rothaug,
author = {Rothaug, Nils and Klix, Simon and Auth, Nicole and B"ocker, Sinan and Puschner, Endres and Becker, Steffen and Paar, Christof},
title = {Towards Unsupervised SEM Image Segmentation for IC Layout Extraction},
booktitle = {Proceedings of the 2023 Workshop on Attacks and Solutions in Hardware Security},
series = {ASHES'23},
year = {2023},
month = {november},
keywords = {ic-layout-extraction;sem-image-segmentation;unsupervised-deep-learning;open-source-dataset},
url = {https://doi.org/10.1145/3605769.3624000},
doi = {10.1145/3605769.3624000},
isbn = {9798400702624},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA}
}