Deep Neural Network Inverse Design of Integrated Photonic Power Splitters

Tahersima, Mohammad H.; Kojima, K.; Koike‐Akino, Toshiaki; Jha, Devesh K.; Wang, Bingnan; Lin, Chih‐Sheng; Parsons, Kieran

doi:10.1038/s41598-018-37952-2

Cited by 281 publications

(143 citation statements)

References 40 publications

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“…Computational modeling and simulations can be used to generate large synthetic datasets. Incorporating ML with computational modeling and simulations has a potential to provide better understanding of biological and physical phenomena, solve illposed inverse problems, optimize complex design problems in a variety of fields (Burrascano et al, 1999;Kim et al, 2007;Tolk, 2015;Hughes et al, 2017;Cohen et al, 2018;Ianni et al, 2018;Pérez et al, 2018;Deist et al, 2019;Kiarashinejad et al, 2019;Meliadò et al, 2019;Tahersima et al, 2019;Vinding et al, 2019). In this work, we have generated a dataset by modeling and simulating MRI gradient-field induced voltage levels on implanted DBS systems, using realistic MRI gradient coil models, six adult anatomical human models (Gosselin et al, 2014) and clinically relevant DBS implant trajectories.…”

Section: Discussionmentioning

confidence: 99%

Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks

Erturk

Panken

Conroy

et al. 2020

Front. Hum. Neurosci.

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Introduction: MRI gradient-fields may induce extrinsic voltage between electrodes and conductive neurostimulator enclosure of implanted deep brain stimulation (DBS) systems, and may cause unintended stimulation and/or malfunction. Electromagnetic (EM) simulations using detailed anatomical human models, therapy implant trajectories, and gradient coil models can be used to calculate clinically relevant induced voltage levels. Incorporating additional anatomical human models into the EM simulation library can help to achieve more clinically relevant and accurate induced voltage levels, however, adding new anatomical human models and developing implant trajectories is time-consuming, expensive and not always feasible.Methods: MRI gradient-field induced voltage levels are simulated in six adult human anatomical models, along clinically relevant DBS implant trajectories to generate the dataset. Predictive artificial neural network (ANN) regression models are trained on the simulated dataset. Leave-one-out cross validation is performed to assess the performance of ANN regressors and quantify model prediction errors.Results: More than 180,000 unique gradient-induced voltage levels are simulated. ANN algorithm with two fully connected layers is selected due to its superior generalizability compared to support vector machine and tree-based algorithms in this particular application. The ANN regression model is capable of producing thousands of gradient-induced voltage predictions in less than a second with mean-squared-error less than 200 mV. Conclusion:We have integrated machine learning (ML) with computational modeling and simulations and developed an accurate predictive model to determine MRI gradientfield induced voltage levels on implanted DBS systems.

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Section: Discussionmentioning

confidence: 99%

Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks

Erturk

Panken

Conroy

et al. 2020

Front. Hum. Neurosci.

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“…We developed an artificial intelligence integrated optimization process using neural networks (NN) that can accelerate optimization by reducing the required number of numerical simulations 24,25 . Also, Tahersima et al 14 used DNN in the inverse direction, i.e., use target performance data (such as transmission spectra) as input, and device design as output. However, the DNN network structure we used (i.e., ResNet) was one-to-one deterministic mapping, which generates only one certain device for every performance set.…”

Section: Introductionmentioning

confidence: 99%

Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter

Tang

Kojima

Koike‐Akino

et al. 2020

Optical Fiber Communication Conference (OFC) 2020

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We propose a novel Conditional Variational Autoencoder (CVAE) model, enhanced with adversarial censoring and active learning, for the generation of 550 nm broad bandwidth (1250 nm to 1800 nm) power splitters with arbitrary splitting ratio. The device footprint is 2.25 × 2.25 µm 2 with a 20 × 20 etched hole combination. It is the first demonstration to apply the CVAE model and the adversarial censoring for the photonics problems. We confirm that the optimized device has an overall performance close to 90% across all bandwidths from 1250 nm to 1800 nm. To the best of our knowledge, this is the smallest broadband power splitter with arbitrary ratio. 7/9

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“…[19,[30][31][32][33][34] The general procedures are mainly setting up DNNs to construct matchings between the structures and EM properties by implementing sufficient EM simulations with variable structure parameters and corresponding spectrum response at selected frequency bands or wavelength. Then, the trained network can be adopted to solve inverse design metasurfaces, [19,30] wavelength demultiplexer, [31] and power splitters [32] using back propagation, where the input takes the EM design targets such as light scattering, transmission, and reflection spectra. This inverse network has one-time structural parameter output, with the advantage of automatic process, fast speed, and less computational resource-consuming.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Designs of Anisotropic Metasurfaces in Ultrawideband Based on Generative Adversarial Networks

Wang

et al. 2020

Advanced Intelligent Systems

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Metasurfaces have been developed as a promising approach for manipulating electromagnetic waves. Recently, deep learning algorithms have been introduced to design metasurfaces, but the network can only output one solution for each desired input and suffers from nonunique issue. To overcome the aforementioned challenges, a deep neural network model for inverse designs of anisotropic metasurfaces with full phase properties in ultrawideband is proposed. Given the target reflection spectra as inputs, the candidate metasurface patterns are generated through a generative adversarial network (GAN), and the corresponding predictions are simply achieved by the accurate forward neural network model to match the target spectra in the whole band with high fidelity. By training the generator and discriminator in GAN in an alternating order combined with setting a threshold of discriminator loss to trigger the phase prediction, the proposed method is much more efficient and consumes less time in the training process. Numerical simulations and experimental results demonstrate that the reflection phases of the generated meta‐atoms have excellent agreements with the given targets, providing an efficient way in automatically designing metasurfaces. The most important advantage of this approach over the previous schemes is to improve the design speed significantly with very good accuracy.

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Deep Neural Network Inverse Design of Integrated Photonic Power Splitters

Cited by 281 publications

References 40 publications

Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks

Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks

Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter

Deep Learning Designs of Anisotropic Metasurfaces in Ultrawideband Based on Generative Adversarial Networks

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