This work introduces a novel inverse design framework that utilizes a progressively trained variational autoencoder (VAE) in conjunction with data augmentation to enable the rapid and efficient generation of structural design parameters for high-performance nanophotonic components.
Abstract
Inverse design of metamaterials is crucial for advancing nanophotonics, but traditional methods face challenges in pixel-by-pixel optimization and computational efficiency. This work introduces a novel inverse design framework that utilizes a progressively trained variational autoencoder (VAE) in conjunction with data augmentation. Our probabilistic generative model directly maps the desired optical spectrum (reflection, transmission, and absorption) to the optimized metamaterial structure, overcoming the limitation of deterministic methods in handling non-unique mappings. This method enables the rapid and efficient generation of structural design parameters for high-performance nanophotonic components, paving the way for next-generation silicon photonic component design and photonic convolutional neural networks.
Metasurfaces are progressively reshaping traditional optical paradigms and pushing the boundaries in complex applications where compact designs are essential. However, the design of metasurfaces demands substantial computational resources to numerically solve Maxwell's equations—particularly for large-scale photonic sy...
Shi-Qi Kuang, Zhi-Zhong Sun, Bo-Yan Fu et al.· PhotoniX· 0 citations
This work presents a conditional deep convolutional generative adversarial network (CDCGAN) for the inverse design of high-absorptance periodic metasurfaces (MSs) with different geometries. The conditioning mechanism encodes geometric and spectral parameters. A key innovation is the inclusion of unit cells with differe...
Jorge Cárdenas, Gabriel Hermosilla, F. Pizarro· Applied Sciences· 0 citations
CoSP, an intelligent inverse design method for RMMs based on a contrastive pretrained large language model (LLM), is proposed, capable of describing material structures with target optical properties in natural language and showing great potential in versatile applications such as thermal management, optical computatio...
Shu-Jie Yang, Yuqi Zhang, Xu-Zhe Zhao et al.· Advancement of science· 0 citations
Optical computing offers a route to address the growing computational demands of machine learning, with inverse‐designed nanophotonic media providing a compact path to passive optical operators. Training such devices end to end remains expensive because each geometry update requires full‐wave forward and adjoint eval...
Azka Maula Iskandar Muda, Uğur Teğin· Nanophotonics· 1 citation
The dual electromagnetically induced transparency (Dual-EIT) effect in photonic crystals exhibits significant potential for applications in multi-channel sensing and slow-light devices. However, conventional forward design based on the finitedifference time-domain (FDTD) method relies heavily on time-consuming paramete...
Hao-Tang Lu· International Conference on...· 0 citations
Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses, but spectrum-to-geometry mapping remains challenging due to non-uniqueness and fine geometric features. This work presents a two-stage deformable-convolutional framework for reconstructing metal--insula...
Waleed I. Waseer, M. S. Jabbar, M. S. Ibrahim et al.· 0 citations
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