Optimization of photonic crystal nanocavities based on deep learning

Asano, Tanemasa; Noda, Susumu

doi:10.1364/oe.26.032704

Cited by 185 publications

(143 citation statements)

References 34 publications

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“…At first, we compare the results of the iterative optimization proposed in this report with the optimization results of the previously reported NN-based optimization method [19], which corresponds to the results obtained in the first round. After the first round of optimization, 1070 samples cavities had been accumulated, and the highest Q factors were 3.0910 5 , 2.4410 5 , and 1.5710 5 for strategies (A), (A+B), (A+C), respectively.…”

Section: Performance Of the Three Strategiesmentioning

confidence: 99%

Iterative optimization of photonic crystal nanocavity designs by using deep neural networks

Asano

Noda

2019

Nanophotonics

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Devices based on two-dimensional photonic-crystal (2D-PC) nanocavities, which are defined by their air hole patterns, usually require a high quality (Q) factor to achieve high performance. We demonstrate that hole patterns with very high Q factors can be efficiently found by the iteration procedure consisting of: machine learning of the relation between the hole pattern and the corresponding Q factor, and new dataset generation based on the regression function obtained by machine learning. First a dataset comprising randomly generated cavity structures and their first principles Q factors is prepared. Then a deep neural network is trained using the initial dataset to obtain a regression function that approximately predicts the Q factors from the structural parameters. Several candidates for higher Q factors are chosen by searching the parameter space using the regression function. After adding these new structures and their first principles Q factors to the training dataset, the above process is repeated. As an example, a standard silicon-based L3 cavity is optimized by this method. A cavity design with a high Q factor exceeding 11 million is found within 101 iteration steps and a total of 8070 cavity structures. This theoretical Q factor is more than twice of the previously reported record values of the cavity designs detected by the evolutionary algorithm and the leaky mode visualization method. It is found that structures with higher Q factors can be detected within less iteration steps by exploring not only the parameter space near the present highest-Q structure but also that distant from the present dataset.

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Section: Performance Of the Three Strategiesmentioning

confidence: 99%

Iterative optimization of photonic crystal nanocavity designs by using deep neural networks

Asano

Noda

2019

Nanophotonics

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“…gaining popularity. Several developments in ML over the past few years has motivated the researchers to explore its potential in the field of photonics, including multimode fibers [5], power splitter [6], plasmonics [7], grating coupler [8], photonic crystals [9], [10], metamaterials [11], photonic modes fields distribution [12], label-free cell classification [13], molecular biosensing [14], optical communications [15], [16] and networking [17], [18].…”

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confidence: 99%

Machine Learning Regression Approach to the Nanophotonic Waveguide Analyses

Chugh

Ghosh

Gulistan

et al. 2019

J. Lightwave Technol.

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Machine learning is an application of artificial intelligence that focuses on the development of computer algorithms which learn automatically by extracting patterns from the data provided. Machine learning techniques can be efficiently used for a problem with a large number of parameters to be optimized and also where it is infeasible to develop an algorithm of specific instructions for performing the task. Here, we combine the finite element simulations and machine learning techniques for the prediction of mode effective indices, power confinement and coupling length of different integrated photonics devices. Initially, we prepare a dataset using COMSOL Multiphysics and then this data is used for training while optimizing various parameters of the machine learning model. Waveguide width, height, operating wavelength, and other device dimensions are varied to record different modal solution parameters. A detailed study has been carried out for a slot waveguide structure to evaluate different machine learning model parameters including number of layers, number of nodes, choice of activation functions, and others. After training, this model is used to predict the outputs for new input device specifications. This method predicts the output for different device parameters faster than direct numerical simulation techniques. Absolute percentage error of less than 5% in predicting an output has been obtained for slot, strip and directional waveguide coupler designs. This study pave the step towards using machine learning based optimization techniques for integrated silicon photonics devices. Index Terms-Machine learning, neural networks, regression, multilayer perceptron, silicon photonics. I. INTRODUCTION M ACHINE learning (ML) technology is being extensively used in many aspects of modern society: web searches, social networking, smartphones, bioinformatics, robotics, chatbots, and self-driving cars [1]. ML techniques are used to classify or detect objects in images, speech to text conversion, pattern recognition, natural language processing, sentiment analysis and recommendations of products/movies for users based on their search preferences. ML algorithms can be trained to perform exceptionally well when it is difficult to analyze the underlying physics and mathematics of the problem [2]. ML algorithms extract patterns from the raw data provided during the training without being explicitly programmed. The learned patterns can be used to make predictions on some other data of interest. ML systems can be trained more efficiently when massive amount of data is present [3], [4]. Recently, research on the application of ML techniques for optical communication systems and nanophotonic devices is

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“…40 Recently, deep learning approaches, based on the artificial neural networks (ANNs), have emerged as a revolutionary and robust methodology in nanophotonics. [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] Indeed, applying the deep learning algorithms to the nanophotonic inverse design can introduce remarkable design flexibility that can go far beyond that of the conventional methods. The inverse design approach works based one the training process, that enables fast prediction of complex optical properties of nanostructures with intricate architectures.…”

Section: Introductionmentioning

confidence: 99%

Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach

Rahmani

et al. 2020

Adv. Photon.

110

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A key concept underlying the specific functionalities of metasurfaces, i.e. arrays of subwavelength nanoparticles, is the use of constituent components to shape the wavefront of the light, on-demand. Metasurfaces are versatile and novel platforms to manipulate the scattering, colour, phase or the intensity of the light. Currently, one of the typical approaches for designing a metasurface is to optimize one or two variables, among a vast number of fixed parameters, such as various materials' properties and coupling effects, as well as the geometrical parameters. Ideally, it would require a multi-dimensional space optimization through direct numerical simulations. Recently, an alternative approach became quite popular allowing to reduce the computational cost significantly based on a deeplearning-assisted method. In this paper, we utilize a deep-learning approach for obtaining high-quality factor (high-Q) resonances with desired characteristics, such as linewidth, amplitude and spectral position. We exploit such high-Q resonances for the enhanced light-matter interaction in nonlinear optical metasurfaces and optomechanical vibrations, simultaneously. We demonstrate that optimized metasurfaces lead up to 400+ folds enhancement of the third harmonic generation (THG); at the same time, they also contribute to 100+ folds enhancement in optomechanical vibrations. This approach can be further used to realize structures with unconventional scattering responses.

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Optimization of photonic crystal nanocavities based on deep learning

Cited by 185 publications

References 34 publications

Iterative optimization of photonic crystal nanocavity designs by using deep neural networks

Iterative optimization of photonic crystal nanocavity designs by using deep neural networks

Machine Learning Regression Approach to the Nanophotonic Waveguide Analyses

Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach

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