{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:34:16Z","timestamp":1772120056444,"version":"3.50.1"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Max-Planck-Institut f\u00fcr Cybersicherheit und Schutz der Privatsph\u00e4re"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cryptogr Eng"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This extended paper presents a novel approach towards unsupervised SEM image segmentation for IC layout extraction. Existing methods typically rely on supervised machine learning with manually labeled training data, requiring re-training and partial annotation when applying them to new datasets. To address this issue, we propose a SEM image segmentation algorithm based on unsupervised deep learning, eliminating the need for manual labeling. We train and evaluate our approach on a real-world dataset comprising 648 SEM images of metal-1 and metal-2 layers from a commercial IC, achieving competitive segmentation error rates well below 1%. Releasing our dataset and algorithm implementations, we allow researchers to apply our approach to their own datasets and evaluate their methods against our dataset, facilitating reproducibility in the field.<\/jats:p>","DOI":"10.1007\/s13389-025-00385-5","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T08:39:10Z","timestamp":1762850350000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing training stability in unsupervised SEM image segmentation for IC layout extraction"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8568-2568","authenticated-orcid":false,"given":"Nils","family":"Rothaug","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2497-9340","authenticated-orcid":false,"given":"Deruo","family":"Cheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9369-2901","authenticated-orcid":false,"given":"Simon","family":"Klix","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2521-7556","authenticated-orcid":false,"given":"Nicole","family":"Auth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9052-126X","authenticated-orcid":false,"given":"Sinan","family":"B\u00f6cker","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1661-6024","authenticated-orcid":false,"given":"Endres","family":"Puschner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7526-5597","authenticated-orcid":false,"given":"Steffen","family":"Becker","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8681-2277","authenticated-orcid":false,"given":"Christof","family":"Paar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"key":"385_CR1","doi-asserted-by":"crossref","unstructured":"Asadizanjani, N., Rahman, M.T., Tehranipoor, M., Asadizanjani, N., Rahman, M.T., Tehranipoor, M.: Physical inspection of integrated circuits. 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