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× smaller mean squared error with
1.3× higher throughput than the state-of-the-art.Comment: Accepted by DAC2