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<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.</p>

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Advancing training stability in unsupervised SEM image segmentation for IC layout extraction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月11日

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  • Nils Rothaug,
  • Deruo Cheng,
  • Simon Klix,
  • Nicole Auth,
  • Sinan Böcker,
  • Endres Puschner,
  • Steffen Becker,
  • Christof Paar

摘要

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.