An Open-Source Artificial Neural Network Model for Polarization-Insensitive Silicon-on-Insulator Subwavelength Grating Couplers

Gostimirovic, Dusan; Ye, Winnie N.

doi:10.1109/jstqe.2018.2885486

Cited by 36 publications

(22 citation statements)

References 18 publications

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“…Given the non-linear responses of individual neurons and their ensembles, the highly non-linear relationship between geometry and response can be properly captured. To date, discriminative networks have been demonstrated to accurately model a wide range of nanoscale electromagnetic systems, including the scattering and chiral properties of plasmonic structures, [83,85] silicon photonic devices, [86] and metasurfaces. [19] Trained discriminative networks can be used to optimize electromagnetic systems in a variety of ways.…”

Section: Inverse Designs Using Artificial Neural Networkmentioning

confidence: 99%

Numerical Optimization Methods for Metasurfaces

Elsawy

Lanteri

Duvigneau

et al. 2020

Laser & Photonics Reviews

146

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In recent years, metasurfaces have emerged as revolutionary tools to manipulate the behavior of light at the nanoscale. These devices consist of nanostructures defined within a single layer of metal or dielectric materials, and they offer unprecedented control over the optical properties of light, leading to previously unattainable applications in flat lenses, holographic imaging, polarimetry, and emission control, amongst others. The operation principles of metaoptics include complex light-matter interactions, often involving insidious near-field coupling effects that are far from being described by classical ray optics calculations, making advanced numerical modeling a requirement in the design process. In this contribution, recent optimization techniques used in the inverse design of high performance metasurfaces are reviewed. These methods rely on the iterative optimization of a Figure of Merit to produce a final device, leading to freeform layouts featuring complex and non-intuitive properties. The concepts in numerical inverse designs discussed herein will push this exciting field toward realistic and practical applications, ranging from laser wavefront engineering to innovative facial recognition and motion detection devices, including augmented reality retro-reflectors and related complex light field engineering.

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Section: Inverse Designs Using Artificial Neural Networkmentioning

confidence: 99%

Numerical Optimization Methods for Metasurfaces

Elsawy

Lanteri

Duvigneau

et al. 2020

Laser & Photonics Reviews

146

View full text Add to dashboard Cite

show abstract

“…The footprint of such a compact coupler was only 1.0× 1.0 μm 2 (see the SEM image). Together with other nanophotonic interfaces, the inverse-designed diamond Regarding DL schemes, Gostimirovic et al [99] discussed on how to use ANN to accelerate preparations of polarization-insensitive grating couplers (see the geometrical configuration in Fig. 8 (b)).…”

Section: On-chip Waveguide-based Couplermentioning

confidence: 99%

Intelligent designs in nanophotonics: from optimization towards inverse creation

Wang

Yan

et al. 2021

PhotoniX

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Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years. Inverse design of functional nanostructures is at the heart of this topic, in which artificial intelligence (AI) furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance. In this review, we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms, manifesting a development trend from performance optimizations towards inverse creations of novel designs. To illustrate interplays between two fields, AI and photonics, we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles, and subsequently review their manifold usages among a set of popular meta-elements. As arranged from levels of individual optimized piece to practical system, we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits. We further comment on a set of open questions including reasonable applications of advanced algorithms, expensive data issue, and algorithm benchmarking, etc. Overall, we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.

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“…Usually several groups of device geometric parameters [ , , … , ] are corresponding to a certain optical response [83]. The mapping from device geometric parameters to optical response is called a forward model [84][85][86][87][88][89][90], while the inverse model describes the mapping from optical response to device geometric parameters [91][92][93]. Both of above-mentioned mapping types have been widely performed through DNNs.…”

Section: Deep Neural Network Assisted Nanophotonics Design For Silicon Platformmentioning

confidence: 99%

“…We take [85] for example to illustrate the process of constructing and training an MLP to surrogate EM simulation of grating couplers. As shown in Figure 6, the geometric parameters (grating pitch, duty cycle, fill factor, fiber angle and polarization) are encoded as data feature x = [x 1 , x 2 , x 3 , x 4 , x 5 ].…”

Section: Multi-layer Perceptronmentioning

confidence: 99%

“…Bias b is updated in a similar way. In the example of reference [85], 9190 data samples are generated via 2D FDTD and separated with a ratio of 85 20.000 data samples to predict the output spectra of power splitter with QR-code like structure [92]. Apart from increasing the layers of an MLP, reducing the dimension of data features is also an efficient way to map high-dimensional data features to the data labels, which could also alleviate the requirement for a large number of training data samples.…”

Section: Multi-layer Perceptronmentioning

confidence: 99%

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Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

et al. 2021

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Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

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An Open-Source Artificial Neural Network Model for Polarization-Insensitive Silicon-on-Insulator Subwavelength Grating Couplers

Cited by 36 publications

References 18 publications

Numerical Optimization Methods for Metasurfaces

Numerical Optimization Methods for Metasurfaces

Intelligent designs in nanophotonics: from optimization towards inverse creation

Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

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