Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices

Ferreira, Adriano da Silva; Santos, Carlos Henrique da Silva; Gonçalves, Marcos Sérgio; Hernandez-Figueroa, Hugo E.

doi:10.1016/j.asoc.2017.12.043

Cited by 30 publications

(10 citation statements)

References 24 publications

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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%

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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Section: Deep Neural Network Assisted Nanophotonics Design For Silicon Platformmentioning

confidence: 99%

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

et al. 2021

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“…These computational requirements are increased when artificial intelligence metaheuristics based on nature-inspired concepts are integrated in these numerical modelling simulations to optimize these devices parameters [19]. As examples, considering bi-dimensional optimizations where devices are designed applying subwavelength lattice optics [20] and photonic band gap structures optimizations [21], [22], as well as for new optical devices design, [23], beam splitters [24], optical power couplers [24], [25], [26], waveguide filters [27] and optical circuit designed by plasmonic nano rods [28] are some of these recent applications.…”

Section: Introductionmentioning

confidence: 99%

“…This method has been validated in the design of a novel minimized 3D optical coupler with 4.2 µm of length, following the 2D concept presented in [25]. However, this 3D model take into account optical waveguides with different height and width and the results show that the efficiency is greater than 90% in wavelength region of 1.55 µm, with is composed of 1260 cylinders in the coupler region, where each of these cylinders were considered as input optimization unknown to be combined.…”

Section: Introductionmentioning

confidence: 99%

Multi-Symmetric Level (MSL) Optimization Technique Based on Genetic Algorithm for Photonic Devices Design

Gonçalves

Silva-Santos²,

Júnior

et al. 2021

J. Microw. Optoelectron. Electromagn. Appl.

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“…This work builds on recent theoretical results showing that it is possible to model nanophotonic structures using ANNs. For example, Ferreira et al [3] and Tahersima et al [4] demonstrated that ANNs could assist with the numerical optimization of waveguide couplers and integrated photonic splitters respectively. In both cases the input parameter space was the entire 2D array of grid points, showing the power of ANNs in blind "black box" approach, though limiting the designer's ability to intuitively adjust input parameters.…”

Section: Introductionmentioning

confidence: 99%

Designing integrated photonic devices using artificial neural networks

Hammond

Camacho

2019

Opt. Express

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We develop and experimentally validate a novel artificial neural network (ANN) design framework for silicon photonics devices that is both practical and intuitive. As case studies, we train ANNs to model both strip waveguides and chirped Bragg gratings using a small number of simple input and output parameters relevant to designers. Once trained, the ANNs decrease the computational cost relative to traditional design methodologies by more than 4 orders of magnitude. To illustrate the power of our new design paradigm, we develop and demonstrate both forward and inverse design tools enabled by the ANN. We use these tools to design and fabricate several integrated Bragg grating devices. The ANN's predictions match very well with the experimental measurements and do not require any post-fabrication training adjustments.

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Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices

Cited by 30 publications

References 24 publications

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

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

Multi-Symmetric Level (MSL) Optimization Technique Based on Genetic Algorithm for Photonic Devices Design

Designing integrated photonic devices using artificial neural networks

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