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<p>With the continuous decrease in the critical dimensions of integrated circuits, mask optimization has become the main challenge in VLSI design. In recent years, thriving machine learning has been gradually introduced in the field of optical proximity correction (OPC). Currently, advanced learning-based frameworks have been limited by low mask printability or large computational overhead. To address these limitations, this paper proposes a learning-based framework named SegNet-OPC, which can generate optimized masks from the target layout at shorter training and turnaround time with higher mask printability. The proposed framework consists of a backbone network and loss terms suitable for mask optimization tasks, followed by a fine-tuning network. The framework yields remarkable improvements over conventional methods, delivering significantly faster turnaround time and superior mask printability and manufacturability. With just 1.25 hours of training, the framework achieves comparable mask complexity while surpassing the state-of-the-art methods, achieving a minimum 3% enhancement in mask printability and an impressive 16.7% improvement in mask manufacturability.</p>

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SegNet-OPC: A Mask Optimization Framework in VLSI Design Flow Based on Semantic Segmentation Network

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Hui Xu,
  • Pan Qi,
  • Fu-Xin Tang,
  • Hua-Guo Liang,
  • Zheng-Feng Huang

摘要

With the continuous decrease in the critical dimensions of integrated circuits, mask optimization has become the main challenge in VLSI design. In recent years, thriving machine learning has been gradually introduced in the field of optical proximity correction (OPC). Currently, advanced learning-based frameworks have been limited by low mask printability or large computational overhead. To address these limitations, this paper proposes a learning-based framework named SegNet-OPC, which can generate optimized masks from the target layout at shorter training and turnaround time with higher mask printability. The proposed framework consists of a backbone network and loss terms suitable for mask optimization tasks, followed by a fine-tuning network. The framework yields remarkable improvements over conventional methods, delivering significantly faster turnaround time and superior mask printability and manufacturability. With just 1.25 hours of training, the framework achieves comparable mask complexity while surpassing the state-of-the-art methods, achieving a minimum 3% enhancement in mask printability and an impressive 16.7% improvement in mask manufacturability.