Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials

Ma, Wei; Cheng, Feng; Liu, Yongmin

doi:10.1021/acsnano.8b03569

Cited by 738 publications

(524 citation statements)

References 55 publications

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“…The rapid penetration of deep-learning framework in biology, genetics, materials science, and physics [32] gives us inspiration, which leads to the deep learning or machine learning design methods. Given a specific pattern, the state-of-the-art method to obtain the phase performance is running numerical simulations with EM simulation packages, whose time consumption is huge.…”

Section: Deep-learning Design and Coding Meta-atomsmentioning

confidence: 99%

Machine‐Learning Designs of Anisotropic Digital Coding Metasurfaces

Zhang

Liu

Wan

et al. 2018

Advcd Theory and Sims

137

109

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Digital coding representations of meta-atoms make it possible to realize intelligent designs of metasurfaces by means of machine learning algorithms. Here, a machine-learning method to design anisotropic digital coding metasurfaces is proposed, and meta-atoms may require any absolute phase values at different positions and under different polarizations. A deep-learning neural network to predict the vast and complex system is proposed, in which only 70 000 training coding patterns are used to train the network. Another 10 000 randomly chosen coding patterns are employed to validate the neural network, showing an accuracy of 90.05% of phase responses with 2°error in the 360°phase. Using the learned network, the correct coding pattern among 18 billion of billions of choices for the required phase can be readily found in a second, finishing automatic design of anisotropic meta-atoms. Three functional 1-bit anisotropic coding metasurfaces are intelligently achieved by the learned network. It is convenient to realize dual-beam scattering with left-handed circular polarization (LHCP) for one beam while right-handed circular polarization (RHCP) for the others, dual-beam scattering with circular polarization for one beam while linear polarization (LP) for the others, and triple-beam scattering with LHCP and RHCP for two beams while LP for the third one.

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Section: Deep-learning Design and Coding Meta-atomsmentioning

confidence: 99%

Machine‐Learning Designs of Anisotropic Digital Coding Metasurfaces

Zhang

Liu

Wan

et al. 2018

Advcd Theory and Sims

137

109

View full text Add to dashboard Cite

show abstract

“…[15][16][17][18][19][20] To identify the optimized parameters of a device, the algorithm computes the gradient, or sensitivity, through the corresponding adjoint problem and updates the parameters along the deepestgradient direction. [27][28][29][30][31] In conjunction with traditional optimization techniques, it has been proved that deep learning can substantially mitigate problems such as the convergence to local minima and the curse of dimensionality in other optimization schema. [21][22][23] The philosophy of the algorithms is to treat photonic structures as a population of individuals, and carry out bio-inspired operations such as selection, reproduction, and mutation to the population in order to identify the optimized individual through evolution.…”

Section: Doi: 101002/adma201904790mentioning

confidence: 99%

Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques

et al. 2019

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controllability of light due to the limited flexibility rendered in periodic metastructures of simple unit cells. To overcome these deficiencies, metasurfaces comprised of multiple meta-atoms, such as gradient and multilayered metasurfaces, have been proposed and developed. [7][8][9] Relying on the collective effects of multiple meta-atoms, these metasurfaces present intriguing properties such as anomalous deflection, [7,10] arbitrary phase control, asymmetric polarization conversion, [8,11] wave-front shaping, [12][13][14] etc., which brings about extensive applications for imaging, optical signal processing, emission control, and much more. Here in our following discussion, we refer to unit cells composed of various meta-atoms as metamolecules, analogous to the hierarchical relationship between atoms and molecules in nature. In our definition of a metamolecule, we assume every two adjacent meta-atoms are not strongly coupled, in which case the overall properties of the metamolecule can be analytically predicted by the properties of its constituent meta-atoms. Such an assumption is valid in most metasurfaces that consist discrete, spatially variant building blocks.Despite the extraordinary properties of metasurfaces made up of metamolecules, designing multiple meta-atoms that collectively function as a device is a time-consuming task that requires labor-intensive trial-and-error simulations. The difficulty of the inverse design of such metamolecules arises from the intricate mechanisms of multistructured systems, the vast number of possible combinations of distinct meta-atoms, as well as the expensive 3D full wave simulations required. Traditionally, a practical solution to such a design follows three steps: 1) specifying a class of geometry with a few parameters as candidate meta-atoms, 2) carrying out parametric sweeps on these parameters, and 3) enumerating possible combinations of meta-atoms to meet the design objective. However, the limitation of the geometry in the strategy largely restricts the variety of the shapes of meta-atoms, which usually does not lead to an optimal solution, even after extensive and expensive simulations.Alongside the evolution of nanophotonics, various methods for expediting the design of photonic structures have been developed. Gradient-based adjoint methods, such as topology optimization, are a class of widely applied approaches for Molecules composed of atoms exhibit properties not inherent to their constituent atoms. Similarly, metamolecules consisting of multiple meta-atoms possess emerging features that the meta-atoms themselves do not possess. Metasurfaces composed of metamolecules with spatially variant building blocks, such as gradient metasurfaces, are drawing substantial attention due to their unconventional controllability of the amplitude, phase, and frequency of light. However, the intricate mechanisms and the large degrees of freedom of the multielement systems impede an effective strategy for the design and optimization of metamolecules. Here, a hybrid artificial-i...

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“…Deep learning (DL)-based design approaches, combined with limited exhaustive searches, have proven to be a potent solver of multi-objective optimization problems by learning the inputoutput relation. [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] DL-based approaches combined with dimensionality reduction (DR) algorithms have been recently developed for design and optimization of EM nanostructures. [20,37,38] More importantly, such novel techniques can provide considerable valuable insight about the dynamics of light-matter interaction in nanostructures with the hope of uncovering new physical phenomena that can be used to form completely new types of devices.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Discovery in Nanophotonics Using Geometric Deep Learning

Kiarashinejad

Zandehshahvar

Abdollahramezani

et al. 2019

Advanced Intelligent Systems

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Herein, a new approach for using the intelligence aspects of artificial intelligence for knowledge discovery rather than device optimization in electromagnetic (EM) nanostructures is presented. This approach uses training data obtained through full‐wave EM simulations of a series of nanostructures to train geometric deep learning algorithms to assess the range of feasible responses as well as the feasibility of a desired response from a class of EM nanostructures. To facilitate the knowledge discovery, this approach combines the dimensionality reduction technique with convex‐hull and one‐class support‐vector‐machine (SVM) algorithms to find the range of the feasible responses in the latent response space of the EM nanostructure. More importantly, the one‐class SVM algorithm can be trained to provide the degree of feasibility of a response from a given nanostructure. This important information can be used to modify the initial structure to an alternative one that can enable an initially unfeasible response. To show the applicability of this approach, it is applied to two important classes of binary metasurfaces (MSs), formed by an array of plasmonic nanostructures, and periodic MSs formed by an array of dielectric nanopillars. These theoretical and experimental results confirm the unique features of this approach for knowledge discovery in EM nanostructures.

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Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials

Cited by 738 publications

References 55 publications

Machine‐Learning Designs of Anisotropic Digital Coding Metasurfaces

Machine‐Learning Designs of Anisotropic Digital Coding Metasurfaces

Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques

Knowledge Discovery in Nanophotonics Using Geometric Deep Learning

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