用分层形态学学习提升光刻掩模优化，精度更高、成本更低。
MorphOPC: Advancing Mask Optimization with Multi-scale Hierarchical Morphological Learning
- 将掩模生成视为局部布局特征的形态学操作序列。
- 在金属层和通孔层上均超越现有方法，印刷保真度更高。
- 适合芯片制造中需要快速高精度掩模优化的场景。
随着特征尺寸缩小到纳米级别，将电路图案精确转移到硅晶圆上变得愈发困难。光学邻近校正（OPC）广泛用于保证图案保真度和可制造性。基于编码器-解码器架构的生成式掩模优化模型可快速生成接近最优的掩模，作为传统OPC的机器学习替代方案。然而，这些模型常无法捕捉目标版图到掩模图案之间的几何变换，导致优化质量不佳。本文将掩模生成建模为对局部布局特征的一系列形态学操作，并提出多尺度分层模型MorphOPC，通过神经形态模块学习这些变换。在基于边缘的OPC及逆光透射（ILT）基准测试中，MorphOPC在金属层和通孔层上均持续优于最先进方法，实现了更高的印刷保真度和更低的制造成本，展现出可扩展掩模优化的强大潜力。
原文摘要 · Abstract (English)
As feature sizes shrink to the nanometer scale, accurately transferring circuit patterns from photomasks to silicon wafers becomes increasingly challenging. Optical proximity correction (OPC) is widely used to ensure pattern fidelity and manufacturability. Recent generative mask optimization models based on encoder-decoder architecture can synthesize near-optimal masks, serving as fast machine learning (ML) surrogates for traditional OPC. However, these models often fail to capture the geometric transformations from target layouts to mask patterns, leading to suboptimal quality. In this work, we formulate mask generation as a sequence of morphological operations on local layout features and propose \textit{MorphOPC}, a multi-scale hierarchical model with neural morphological modules to learn these transformations. Experiments on edge-based OPC and ILT benchmarks across metal and via layers show that \textit{MorphOPC} consistently outperforms state-of-the-art methods, achieving higher printing fidelity and lower manufacturing cost, demonstrating strong potential for scalable mask optimization.
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