Advancing Training Stability in Unsupervised SEM ImageSegmentation for IC Layout Extraction

Abstract 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.