面向半导体光刻的基于光学衍射的卷积
Optical Diffraction-based Convolution for Semiconductor Lithography
把光学衍射的相位传播先验写进卷积核（optical phase kernel），为需要物理一致性的成像逆问题提供了一种可复用的「物理算子嵌入网络」范式，可迁移到计算成像、相位恢复等任务。
原文依据 OptiCo employs an optical phase kernel to model phase variations resulting from light propagation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近年来，对更小、更强大半导体的需求不断增长，凸显了光刻的关键作用——光刻是半导体制造中负责精确掩码设计与晶圆图案化的关键环节。为满足这些需求，半导体行业越来越多地采用计算光刻，利用机器学习和深度学习技术加速光刻技术的进步。尽管计算光刻领域已有多种研究努力并取得了成功，但现有方法仍缺乏对物理原理的显式融入。这一缺口限制了现有方法充分刻画光刻行为中固有复杂物理现象的能力。为弥合这一缺口，我们提出 OptiCo，一种新颖的卷积神经网络，将光学衍射原理无缝集成到其架构中。OptiCo 的核心是采用光学相位核来建模光传播引起的相位变化，从而有效刻画光、掩码与晶圆之间的物理相互作用。我们在半导体光刻基准上评估 OptiCo，其在掩码优化任务中展现出优越性能，并在分布外数据集上具有显著的泛化能力。
英文原文摘要 1267 字符
In recent years, the increasing demand for smaller and more powerful semiconductors highlighted the critical role of lithography--a key stage in semiconductor manufacturing responsible for precise mask design and wafer patterning. To meet these demands, the semiconductor industry has increasingly adopted computational lithography, employing machine learning and deep learning techniques to accelerate advancements in lithographic technology. Despite the various research efforts and successes in computational lithography, there remains a lack of explicit incorporation of physical principles. This gap limits the ability of existing methods to fully capture the complex physical phenomena inherent in lithography behaviors. To bridge this gap, we propose OptiCo, a novel convolutional neural network that seamlessly integrates optical diffraction principles into its architecture. At its core, OptiCo employs an optical phase kernel to model phase variations resulting from light propagation, effectively capturing the physical interactions among light, masks, and wafers. We evaluate OptiCo on semiconductor lithography benchmarks, demonstrating its superior performance in mask optimization tasks, with its remarkable generalization capabilities in OOD datasets.