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

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: 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 = {doi.org/10.1145/3605769.3624000}, doi = {10.1145/3605769.3624000}, isbn = {9798400702624}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA} }