Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning

@article{Pearson2022InverseDA,
  title={Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning},
  author={Stewart Pearson and Parinaz Naseri and Sean Victor Hum},
  journal={ArXiv},
  year={2022},
  volume={abs/2204.00433}
}
Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the… 

References

SHOWING 1-10 OF 39 REFERENCES

A Combined Machine-Learning / Optimization-Based Approach for Inverse Design of Nonuniform Bianisotropic Metasurfaces

This paper proposes an end-to-end systematic and efficient approach where the designer inputs high-level far-field constraints such as nulls, sidelobe levels, and main beam level(s); and a 3-layer nonuniform passive, lossless, omega-type bianisotropic electromagnetic metasurface design to satisfy them is returned.

A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces

A generative machine learning (ML)-based approach is proposed to solve this one-to-many mapping and automate the inverse design of dual- and triple-layer metasurfaces and solve multiobjective optimization problems by synthesizing thin structures composed of potentially brand-new scatterer designs.

Simulator-based training of generative neural networks for the inverse design of metasurfaces

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.

Generative Model for the Inverse Design of Metasurfaces

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.

Metasurface inverse design using machine learning approaches

AMID greatly simplifies conventional methods that call for not only sufficient professional knowledge but also trial and error through simulation softwares and verifies the availability of AMID and improves the design efficiency in the meanwhile.

Optimization of Electromagnetic Metasurface Parameters Satisfying Far-Field Criteria

Electromagnetic metasurfaces offer the capability to realize arbitrary power-conserving field transformations. These field transformations are governed by the generalized sheet transition conditions,

Multiplexed supercell metasurface design and optimization with tandem residual networks

This study demonstrates the feasibility of high-dimensional supercell inverse design with deep neural networks, which is applicable to complex nanophotonic structures composed of multiple subunit elements that exhibit coupling.

Multifunctional Metasurface Design with a Generative Adversarial Network

A generative adversarial network that can tackle the problem and generate meta‐atom/metasurface designs to meet multifunctional design goals is presented and the ability of the network to generate distinct classes of structures with similar EM responses but different physical features can provide added latitude to accommodate other considerations.

Gradient-Based Electromagnetic Inversion for Metasurface Design Using Circuit Models

A gradient-based optimization algorithm is presented that is capable of directly designing a metasurface at the circuit parameter level for a desired field (amplitude and phase) pattern or a desired

Fast and Accurate Optimization of Metasurfaces with Gradient Descent and the Woodbury Matrix Identity

An accelerated gradient descent optimization algorithm is presented that enables the direct optimization of finite-size metasurfaces modeled using integral equations and allows the inverse of the perturbed impedance matrix to be computed at a low cost.