MOCLIP: a foundation model for large-scale nanophotonic inverse design

Foundation models are transforming artificial intelligence by enabling generalizable, data-efficient solutions across diverse domains and applications. However, the lack of large and diverse datasets remains a key barrier to their development in nanophotonics. This work presents MOCLIP (Metasurface Optics Contrastive Learning Pretrained), a nanophotonic foundation model that encodes metasurfaces' structural and spectral information into a shared latent space via contrastive learning, using an experimentally acquired dataset with sample density approaching the scale of ImageNet-1K. The study demonstrates MOCLIP's inverse design capabilities, including high-throughput zero-shot prediction at 2 ⋅ 10⁵ samples per second, full-wafer design of an entire 4-inch substrate in minutes, and latent space optimization reaching 95% accuracy. This work further introduces an optical information storage concept that leverages MOCLIP to achieve a storage density of 0.1 Gbit/mm² at the resolution limit, surpassing commercial optical media by a factor of six. Together, these results position MOCLIP as a scalable, versatile platform for next-generation photonic design and data-driven applications.