用扩散模型逆向设计光学多层膜，精准匹配目标光谱。
Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models
- 将多层膜表示为材料-厚度序列，用扩散模型从光谱反推结构。
- 相比现有方法，光谱误差降低2.9至3.45倍，精度显著提升。
- 适合光子器件设计、材料逆向工程等领域的研究人员使用。
光学多层膜的逆向设计旨在根据目标光谱推断各层材料、厚度与排列顺序，因设计空间大且解不唯一而长期困难。本文提出 exttt{OptoLlama}，一种基于掩码扩散语言模型的逆向薄膜设计方法。将多层膜表示为材料-厚度标记序列， exttt{OptoLlama}以反射率、吸收率和透射率光谱为条件，学习从光学响应到结构的概率映射。在包含3,000个目标的测试集上， exttt{OptoLlama}相较最近邻模板基线，平均绝对光谱误差降低2.9倍；相较当前最优数据驱动方法 exttt{OptoGPT}，误差降低3.45倍。对设计及专家定义目标的案例研究显示，模型能复现典型光谱特征，并恢复分布式布拉格反射器等物理上合理的堆叠模式。结果表明，基于扩散的序列建模是逆向光子设计的强大框架。
原文摘要 · Abstract (English)
Inverse design of optical multilayer stacks seeks to infer layer materials, thicknesses, and ordering from a desired target spectrum. It is a long-standing challenge due to the large design space and non-unique solutions. We introduce \texttt{OptoLlama}, a masked diffusion language model for inverse thin-film design from optical spectra. Representing multilayer stacks as sequences of material-thickness tokens, \texttt{OptoLlama} conditions generation on reflectance, absorptance, and transmittance spectra and learns a probabilistic mapping from optical response to structure. Evaluated on a representative test set of 3,000 targets, \texttt{OptoLlama} reduces the mean absolute spectral error by 2.9-fold relative to a nearest-neighbor template baseline and by 3.45-fold relative to the state-of-the-art data-driven baseline, called \texttt{OptoGPT}. Case studies on designed and expert-defined targets show that the model reproduces characteristic spectral features and recovers physically meaningful stack motifs, including distributed Bragg reflectors. These results establish diffusion-based sequence modeling as a powerful framework for inverse photonic design.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。