Towards Photonic Band Diagram Generation with Transformer-Latent Diffusion Models
This work addresses a bottleneck in photonic crystal design for researchers and engineers, offering a novel surrogate modeling method that is incremental in applying existing AI techniques to a new domain.
The paper tackles the computationally expensive problem of generating photonic band diagrams by introducing a transformer-latent diffusion model, achieving the first approach for this task with potential for generalization to 3D structures.
Photonic crystals enable fine control over light propagation at the nanoscale, and thus play a central role in the development of photonic and quantum technologies. Photonic band diagrams (BDs) are a key tool to investigate light propagation into such inhomogeneous structured materials. However, computing BDs requires solving Maxwell's equations across many configurations, making it numerically expensive, especially when embedded in optimization loops for inverse design techniques, for example. To address this challenge, we introduce the first approach for BD generation based on diffusion models, with the capacity to later generalize and scale to arbitrary three dimensional structures. Our method couples a transformer encoder, which extracts contextual embeddings from the input structure, with a latent diffusion model to generate the corresponding BD. In addition, we provide insights into why transformers and diffusion models are well suited to capture the complex interference and scattering phenomena inherent to photonics, paving the way for new surrogate modeling strategies in this domain.