MOCLIP用对比学习构建纳米光子学基础模型，实现超高速逆向设计。
MOCLIP: A Foundation Model for Large-Scale Nanophotonic Inverse Design
- 通过对比学习将几何与光谱特征对齐在共享潜空间中。
- 每秒可完成20万样本的零样本逆向设计，1分钟内完成4英寸晶圆设计。
- 适用于高密度光子器件设计与光学信息存储，适合芯片研发人员。
基础模型（FM）正推动人工智能向通用化、数据高效化发展。然而，纳米光子学领域缺乏大规模多样数据集，制约了基础模型的发展。本文提出MOCLIP（Metasurface Optics Contrastive Learning Pretrained），一种集成超表面几何与光谱特征的纳米光子学基础模型，采用对比学习方法，基于实验采集的数据集（样本量相当于ImageNet-1K）对齐几何与光谱表示。研究表明，MOCLIP可实现每秒0.2百万样本的高通量零样本逆向设计，支持在数分钟内完成4英寸晶圆上高密度超表面的布局设计；其生成式潜空间优化可达97%准确率；并提出了基于MOCLIP的光学信息存储概念，在分辨率极限下实现0.1 Gbit/mm²的存储密度，较商业光介质提升6倍。这些成果使MOCLIP成为下一代光子设计与数据驱动应用的可扩展、多功能平台。
原文摘要 · Abstract (English)
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.
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