Free-Form Diffractive Metagrating Design Based on Generative Adversarial Networks

Jiang, Jiaqi; Sell, D. D.; Hoyer, Stephan; Hickey, Jason; Yang, Jianji; Fan, Jonathan A.

doi:10.1021/acsnano.9b02371

Cited by 287 publications

(245 citation statements)

References 39 publications

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“…In addition to adjoint methods, genetic algorithms and related variations also play important roles in the design of photonic structures. [26,[32][33][34] Despite the development of techniques for the optimization of photonic structures, the inverse design of metasurfaces with metamolecules, of which the degrees of freedom are astronomical, is still not resolved. Both gradient-based methods and genetic algorithms require immense amount of simulations.…”

Section: Doi: 101002/adma201904790mentioning

confidence: 99%

“…[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. [26,[32][33][34] Despite the development of techniques for the optimization of photonic structures, the inverse design of metasurfaces with metamolecules, of which the degrees of freedom are astronomical, is still not resolved. Although the collective properties of metamolecules can be predicted by individually simulating each meta-atom, the enormous number of possible combinations of candidate structures impedes efficient designing using state-of-the-art optimization techniques.…”

Section: Doi: 101002/adma201904790mentioning

confidence: 99%

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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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Section: Doi: 101002/adma201904790mentioning

confidence: 99%

Section: Doi: 101002/adma201904790mentioning

confidence: 99%

Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques

et al. 2019

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“…GLOnets. GLOnet is a global optimizer based on the training of a generative neural network and can output highly efficient topology-optimized metasurfaces that operate near or at the physical limits of structured media engineering [26,45]. A key feature of the optimization approach is that the network initially generates a distribution of devices that broadly samples the design space, and it then shifts and refines this distribution towards favorable design space regions over the course of optimization.…”

Section: Metagrating Optimization Algorithmsmentioning

confidence: 99%

“…These algorithms range from conventional optimization methods, such as evolutionary, annealing [21], and genetic algorithms [22], to those based on gradient-based topology optimization [23][24][25]. More recently, a number of groups have harnessed machine learning as a means to facilitate electromagnetic simulations and the design process [26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

MetaNet: a new paradigm for data sharing in photonics research

Jiang¹,

Lupoiu²,

Wang³

et al. 2020

Opt. Express

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Optimization methods are playing an increasingly important role in all facets of photonics engineering, from integrated photonics to free space diffractive optics. However, efforts in the photonics community to develop optimization algorithms remain uncoordinated, which has hindered proper benchmarking of design approaches and access to device designs based on optimization. We introduce MetaNet, an online database of photonic devices and design codes intended to promote coordination and collaboration within the photonics community.Using metagratings as a model system, we have uploaded over one hundred thousand device layouts to the database, as well as source code for implementations of local and global topology optimization methods. Further analyses of these large datasets allow the distribution of optimized devices to be visualized for a given optimization method. We expect that the coordinated research efforts enabled by MetaNet will expedite algorithm development for photonics design.

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“…The stochastic nature of the generative models enables the exploration of the solution space in a global way. Consolidating with traditional optimization techniques, GAN and VAE are able to discover the topology of nanostructures with improved efficiency and robustness [29][30][31] .In the problems of inverse design of photonic structures, optimizing the topology of a photonic structure with arbitrary shape is a long-sought-after goal. Typically, the topology of photonic structures is represented in binary images.…”

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confidence: 99%

Topological encoding method for data-driven photonics inverse design

Liu¹,

Zhu²,

Cai³

2020

Opt. Express

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Data-driven approaches have been proposed as effective strategies for the inverse design and optimization of photonic structures in recent years. In order to assist data-driven methods for the design of topology of photonic devices, we propose a topological encoding method that transforms photonic structures represented by binary images to a continuous sparse representation. This sparse representation can be utilized for dimensionality reduction and dataset generation, enabling effective analysis and optimization of photonic topologies with data-driven approaches. As a proof of principle, we leverage our encoding method for the design of two dimensional non-paraxial diffractive optical elements with various diffraction intensity distributions. We proved that our encoding method is able to assist machine-learning-based inverse design approach for accurate and global optimization.With the fast evolution of machine learning (ML) and deep learning (DL) techniques 12 , data-driven methods are emerging as an alternative way to discover and design photonic structures and devices 13 . Feedforward neural networks are leveraged for the approximation of the photonics systems with tens to hundreds of parameters, and have been utilized to successfully optimize photonic structures such as photonic crystals 14,15 , waveguide 16,17 , chiral metamaterials 18 , and metasurfaces 19 . When the degree of freedom (DOF) of the photonic system grows to thousands and more, convolutional neural networks (CNN) are adopted for the accurate prediction of the physical responses with much lower computational complexity 20,21 . Photonic structures represented in pixelated images, for example, are usually processed by CNNs to reduce the DOF for further optimization. Additionally, generative models, such as variational autoencoder (VAE) 22 and generative adversarial network (GAN) 23,24 , are utilized for the design of high DOF metasurface nanostructures in an expeditious way [25][26][27][28] . The stochastic nature of the generative models enables the exploration of the solution space in a global way. Consolidating with traditional optimization techniques, GAN and VAE are able to discover the topology of nanostructures with improved efficiency and robustness [29][30][31] .In the problems of inverse design of photonic structures, optimizing the topology of a photonic structure with arbitrary shape is a long-sought-after goal. Typically, the topology of photonic structures is represented in binary images. Because of the discretization and the high DOF of binary images, optimization is likely stuck in local minima. Although generative models are able to discover new topologies so as to approach global minimum, the bias of the training dataset and the limited capacity of the network cause incompleteness of the solution space, i.e., the global minimum may not be included in the space defined by the training dataset. Here, we propose an encoding method that is able to transform the binary image to a continuous sparse representation. This encoding appro...

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Free-Form Diffractive Metagrating Design Based on Generative Adversarial Networks

Cited by 287 publications

References 39 publications

Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques

Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques

MetaNet: a new paradigm for data sharing in photonics research

Topological encoding method for data-driven photonics inverse design

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