• Corpus ID: 209532128

A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks

@article{An2020AFD,
  title={A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks},
  author={Sensong An and Bowen Zheng and Mikhail Y. Shalaginov and Hong Tang and Hang Li and Li Zhou and Jun Ding and Anuradha Murthy Agarwal and Clara Rivero‐Baleine and Myungkoo Kang and Kathleen A. Richardson and Tian Gu and Juejun Hu and Clayton Fowler and Hualiang Zhang},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.00121}
}
Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error method to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of different meta-atom designs with different physical and geometric parameters, which normally demands huge computational resources. In this… 

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References

SHOWING 1-10 OF 29 REFERENCES
A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks
TLDR
A deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices.
Simulator-based training of generative neural networks for the inverse design of metasurfaces
TLDR
This work presents a new type of population-based global optimization algorithm for metasurfaces that is enabled by the training of a generative neural network and observes that the distribution of devices generated by the network continuously shifts towards high performance design space regions over the course of optimization.
Plasmonic nanostructure design and characterization via Deep Learning
TLDR
Rising to the challenge, Haim Suchowski and colleagues from Tel Aviv University in Israel have developed an innovative technique that uses Deep Neural Networks to model the complex relationships between light-matter interactions, allowing them to characterise nanostructures based on their far-field optical responses.
Generative Model for the Inverse Design of Metasurfaces
TLDR
This work identifies a solution to circumvent this conventional design procedure by means of a deep learning architecture to expedite the discovery and design of metasurfaces for tailored optical responses in a systematic, inverse-design manner.
Global optimization of dielectric metasurfaces using a physics-driven neural network
We present a global optimizer, based on a conditional generative neural network, which can output ensembles of highly efficient topology-optimized metasurfaces operating across a range of parameters.
Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials.
TLDR
A deep-learning-based model is reported, comprising two bidirectional neural networks assembled by a partial stacking strategy, to automatically design and optimize three-dimensional chiral metamaterials with strong chiroptical responses at predesignated wavelengths.
Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques
TLDR
A hybrid artificial-intelligence-based framework consolidating compositional pattern-producing networks and cooperative coevolution to resolve the inverse design of metamolecules in metasurfaces is proposed, revealing a promising candidate approach to expedite the design of large-scale metAsurfaces in a labor-saving, systematic manner.
Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures.
TLDR
It is demonstrated how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures.
Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy
TLDR
This work proposes to represent metamaterials and model the inverse design problem in a probabilistically generative manner, enabling to elegantly investigate the complex structure–performance relationship in an interpretable way, and solve the one‐to‐many mapping issue that is intractable in a deterministic model.
A Hybrid Strategy for the Discovery and Design of Photonic Structures
TLDR
This work represents an efficient, on-demand, and automated approach for the inverse design of photonic structures with subwavelength features and requires no prior knowledge of the geometry of thePhotonic structures, and allows joint optimization of the dimensional parameters.
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