A Hybrid Strategy for the Discovery and Design of Photonic Structures

Liu, Zhaocheng; Raju, Lakshmi; Zhu, Dayu; Cai, Wenshan

doi:10.1109/jetcas.2020.2970080

Cited by 67 publications

(68 citation statements)

References 33 publications

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“…We note that the relative ease with which standard ML techniques can be adapted and applied to PhCs, as shown here, suggests a promising application space for datadriven approaches in photonics more generally. Especially within generative modeling, a large suite of ML techniques exists that point to several opportunities for data-driven inverse photonic design, some of which have already been explored: among them, variational autoencoders [69] exemplify a natural alternative [70] to GANs for photonic inverse design [71,72], as does the related approach of bidirectional neural networks [73,74]. Further, the ML application-space for PhCs extends beyond the periodic settings considered here: for instance, both isolated and aperiodic systems, such as PhC defect cavities and quasiperiodic PhCs, may be explored with similar ML techniques, e.g.…”

Section: Discussionmentioning

confidence: 99%

Predictive and generative machine learning models for photonic crystals

et al. 2020

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The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences.

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Section: Discussionmentioning

confidence: 99%

Predictive and generative machine learning models for photonic crystals

et al. 2020

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“…Even though no generalized and fast inverse design method has as yet been reported, rapid progress has been made in this direction over the last two years. 13,[36][37][38][39][40][41][42][43] The overall trend in these studies so far is, that for every specific inverse problem using a particular geometric model, a neural network needs to be designed in a time demanding and computationally very expensive process, involving hyper-parameter optimization, training data generation, training and extensive testing.…”

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confidence: 99%

Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures

2019

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Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate 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. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal methods for design and analysis of nanophotonic systems.

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“…As we can observe, the two distinct topologies can be smoothly transformed by linearly interpolating their latent vectors. This property is indispensable for the fast convergence when evolution strategy (ES) is utilized for the topology optimization 32 . It is noteworthy that linear interpolation does not always result in continuous topological transformation, especially when the latent space is high dimensional and the topologies of the two patterns are significantly distinct.…”

Section: B Continuity Of the Latent Spacementioning

confidence: 99%

“…In order to globally optimize the topology of the grating patterns, we adopt the modified evolution strategy (ES) 32 as shown in Fig. 3(c).…”

Section: Designing Non-paraxial Diffractive Optical Elements (Does)mentioning

confidence: 99%

“…This encoding approach can be used for data generation and dimensionality reduction of photonic structures, augmenting the capacity of ML models for analyzing the dataset, and enhancing the likelihood of achieving the global minimum in the optimization problems. As a proof of principle, we consolidate the proposed encoding method and a DL-based optimization framework 26,32,33 to inverse design and optimize non-paraxial diffractive optical elements (DOE) with various diffraction intensity distributions. Traditionally, iterative Fourier transform algorithm (IFTA) is used for the design of binary phase mask of the DOE with small diffraction angle 34,35 .…”

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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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A Hybrid Strategy for the Discovery and Design of Photonic Structures

Cited by 67 publications

References 33 publications

Predictive and generative machine learning models for photonic crystals

Predictive and generative machine learning models for photonic crystals

Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures

Topological encoding method for data-driven photonics inverse design

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