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).
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.
We additionally provide labels for tracks and vias on the M2 layer, which are included as .svg
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.
The implementation of our approach, as well as accompanying evaluation and utility routines can be found in the following GitHub repository: https://github.com/emsec/unsupervised-ic-sem-segmentation
Please make sure to always cite our study when using any part of our data set or code for your own research publications!
@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}
}