{"ID":2837482,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18980","arxiv_id":"2511.18980","title":"MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design","abstract":"Foundation models (FM) are transforming artificial intelligence by enabling generalizable, data-efficient solutions across different domains for a broad range of applications. However, the lack of large and diverse datasets limits the development of FM in nanophotonics. This work presents MOCLIP (Metasurface Optics Contrastive Learning Pretrained), a nanophotonic foundation model that integrates metasurface geometry and spectra within a shared latent space. MOCLIP employs contrastive learning to align geometry and spectral representations using an experimentally acquired dataset with a sample density comparable to ImageNet-1K. The study demonstrates MOCLIP inverse design capabilities for high-throughput zero-shot prediction at a rate of 0.2 million samples per second, enabling the design of a full 4-inch wafer populated with high-density metasurfaces in minutes. It also shows generative latent-space optimization reaching 97 percent accuracy. Finally, we introduce an optical information storage concept that uses MOCLIP to achieve a density of 0.1 Gbit per square millimeter at the resolution limit, exceeding commercial optical media by a factor of six. These results position MOCLIP as a scalable and versatile platform for next-generation photonic design and data-driven applications.","short_abstract":"Foundation models (FM) are transforming artificial intelligence by enabling generalizable, data-efficient solutions across different domains for a broad range of applications. However, the lack of large and diverse datasets limits the development of FM in nanophotonics. This work presents MOCLIP (Metasurface Optics Con...","url_abs":"https://arxiv.org/abs/2511.18980","url_pdf":"https://arxiv.org/pdf/2511.18980v1","authors":"[\"S. Rodionov\",\"A. Burguete-Lopez\",\"M. Makarenko\",\"Q. Wang\",\"F. Getman\",\"A. Fratalocchi\"]","published":"2025-11-24T10:54:19Z","proceeding":"physics.optics","tasks":"[\"physics.optics\",\"cs.AI\"]","methods":"[]","has_code":false}