Towards Unsupervised SEM Image Segmentation for IC Layout Extraction


Conference / Medium


Christof Paar Steffen Becker Endres Puschner Sinan Böcker Nicole Auth Simon Klix Nils Rothaug

Research Hub

Research Hub B: Eingebettete Sicherheit

Research Challenges

RC 4: Platform Trojans


This 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.


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