Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead. The advancement of machine learning (ML)-based lithography models alleviates the trade-offs between manufacturing process expense and capability. However, all previous methods regard the lithography system as an image-to-image black box mapping, utilizing network parameters to learn by rote mappings from massive mask-to-aerial or mask-to-resist image pairs, resulting in poor generalization capability. In this paper, we propose a new ML-based paradigm disassembling the rigorous lithographic model into non-parametric mask operations and learned optical kernels containing determinant source, pupil, and lithography information. By optimizing complex-valued neural fields to perform optical kernel regression from coordinates, our method can accurately restore lithography system using a small-scale training dataset with fewer parameters, demonstrating superior generalization capability as well. Experiments show that our framework can use 31\% of parameters while achieving 69$\times$ smaller mean squared error with 1.3$\times$ higher throughput than the state-of-the-art.

翻译：光刻是集成电路制造的基础，需要巨大的计算开销。基于机器学习（ML）的光刻模型的发展缓解了制造工艺成本与能力之间的权衡。然而，以往所有方法都将光刻系统视为图像到图像的黑盒映射，利用网络参数从海量掩模到空间像或掩模到光刻胶图像对中机械学习映射关系，导致泛化能力较差。本文提出一种新的基于ML的范式，将严格的光刻模型解耦为非参数掩模操作和包含决定性光源、光瞳及光刻信息的学习型光学核。通过优化复值神经场从坐标进行光学核回归，我们的方法能够利用小规模训练数据集和较少参数准确还原光刻系统，并展现出优越的泛化能力。实验表明，与最先进方法相比，我们的框架仅使用31%的参数，即可实现均方误差缩小69倍、吞吐量提升1.3倍的效果。