用扩散模型逆向设计超薄光学元件，30分钟内完成高精度光路分裂器
Inverse Design of Diffractive Metasurfaces Using Diffusion Models
- 用扩散模型根据目标光分布反推超表面结构
- 设计两种光路分裂器误差低，耗时不足30分钟
- 适合光学器件设计与生成式AI交叉研究者
超表面是由亚波长结构构成的超薄光学元件，可精确调控光场。其逆向设计——即确定能产生特定光学响应的几何结构——因结构与光学特性间存在复杂非线性关系而极具挑战，通常需专家调参，易陷入局部最优，且计算开销大。本文将扩散模型的生成能力融入计算设计流程：利用RCWA仿真器生成包含超表面几何结构及其远场散射图样的训练数据；随后训练一个条件扩散模型，从指定波长下连续支持带采样的目标空间功率分布，预测元原子的几何形状与高度。模型训练完成后，可通过RCWA引导的后验采样直接生成低误差超表面，或作为传统优化方法的初始值。我们在均匀强度分束器和偏振分束器的设计上验证了该方法，均在30分钟内实现低误差结果。为促进数据驱动的超表面设计研究，我们公开发布代码与数据集。
原文摘要 · Abstract (English)
Metasurfaces are ultra-thin optical elements composed of engineered sub-wavelength structures that enable precise control of light. Their inverse design - determining a geometry that yields a desired optical response - is challenging due to the complex, nonlinear relationship between structure and optical properties. This often requires expert tuning, is prone to local minima, and involves significant computational overhead. In this work, we address these challenges by integrating the generative capabilities of diffusion models into computational design workflows. Using an RCWA simulator, we generate training data consisting of metasurface geometries and their corresponding far-field scattering patterns. We then train a conditional diffusion model to predict meta-atom geometry and height from a target spatial power distribution at a specified wavelength, sampled from a continuous supported band. Once trained, the model can generate metasurfaces with low error, either directly using RCWA-guided posterior sampling or by serving as an initializer for traditional optimization methods. We demonstrate our approach on the design of a spatially uniform intensity splitter and a polarization beam splitter, both produced with low error in under 30 minutes. To support further research in data-driven metasurface design, we publicly release our code and datasets.
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