MOCLIP: a foundation model for large-scale nanophotonic inverse design.
basic_science · Level V
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- Record sourced from PubMed, PMID 42749714.
- Also identified by DOI 10.1038/s41467-026-76714-x.
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Abstract
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 ⋅ 10<sup>5</sup> 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/mm<sup>2</sup> 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.