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

Tang, Yingheng; Kojima, K.; Koike‐Akino, Toshiaki; Wang, Ye; Wu, Pengxiang; Tahersima, Mohammad H.; Jha, Devesh K.; Parsons, Kieran; Qi, Minghao

doi:10.1364/ofc.2020.th1a.1

Cited by 10 publications

(10 citation statements)

References 28 publications

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“…The existing literature on power splitters lacks clarity regarding the determination and optimization of the radius of the dielectric holes [31], [32]. To achieve the best performance of the power splitter, hole radii are optimized in terms of transmittance and reflectance.…”

Section: B Hole Radius Optimizationmentioning

confidence: 99%

A 1.55 μm Wideband 1 × 2 Photonic Power Splitter With Arbitrary Ratio: Characterization and Forward Modeling

et al. 2022

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Chip-based photonic systems have undergone substantial progress over the last decade. However, the realization of photonic devices still depends largely on intuition-based trial-and-error methods, with a limited focus on characteristic analysis. In this work, we demonstrate an in-depth investigation of photonic power splitters by considering the transmission properties of 16,000 unique ultra-compact silicon-based structures engraved with SiO 2 , Al 2 O 3 , and Si 3 N 4 nanoholes. The characterization has been performed using finite-difference time-domain (FDTD) simulations for each dielectric material and both TE and TM polarizations at the fundamental modes in a wideband optical communication spectrum ranging from 1.45 to 1.65 µm. The corresponding transmissions, splitting ratio, and reflection loss were calculated, generating a dataset that can be used for both forward and inverse modeling purposes, using Machine Learning (ML) and Deep Learning (DL) algorithms. With an optimized hole radius of 35 nm, the proposed device area footprint of 2 µm × 2 µm is among the smallest with the best transmission reported to date. Si 3 N 4 holes show excellent transmission because they offer 90% transmittance in 96% of the data while exhibiting maximum fabrication tolerance. Forward modeling analysis, predicting the transmission properties, was performed using both Linear Model (LM) and Artificial Neural Network (ANN), where LM showed marginally better accuracy than ANN in foreseeing the transmittance. The proposed observation will aid in achieving robust, optimized optical power splitters with a wide range of splitting ratios in lesser time.INDEX TERMS Forward modeling, machine learning, optical power splitter, artificial neural network.

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Section: B Hole Radius Optimizationmentioning

confidence: 99%

A 1.55 μm Wideband 1 × 2 Photonic Power Splitter With Arbitrary Ratio: Characterization and Forward Modeling

et al. 2022

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“…The authors also demonstrate transfer learning to other microstructure shape classes as well as inverse design of multiple microstructures forming a meta-mirror with desired properties. DGMs have also been applied to nano-scale photonic devices, such as an optic broadband power splitter as in Tang et al [91].…”

Section: Microstructure Nanostructure and Metamaterialsmentioning

confidence: 99%

Deep Generative Models in Engineering Design: A Review

Regenwetter¹,

Nobari²,

Ahmed³

2021

Preprint

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Automated design synthesis has the potential to revolutionize the modern human design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may be the key to such automated design synthesis and is a research subject of great importance. We present a review and analysis of Deep Generative Learning models in engineering design. Deep Generative Models (DGMs) typically leverage deep networks to learn from an input dataset and learn to synthesize new designs. Recently, DGMs such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), feedforward Neural Networks (NNs) and certain Deep Reinforcement Learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in Engineering Design has skyrocketed since 2016. Anticipating continued growth, we conduct a review of recent advances with the hope of benefitting researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling complex constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion we identify possible solution pathways as key areas on which to target future work.

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“…A few studies have tried to formulate the inverse design problem as modeling a conditional probability distribution of geometry/design for a given optical response 18,19 using a conditional generative adversarial network (cGAN) 20 and variational autoencoders (VAEs) 21 . Additionally, global optimization techniques such as genetic algorithms (GAs) have also proved useful for inverse nanophotonic design 22,23 .…”

Section: Moreover the Non-uniqueness Of Structural Designs And High mentioning

confidence: 99%

A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces

Mall

Patil

Sethi

et al. 2020

Sci Rep

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The conventional approach to nanophotonic metasurface design and optimization for a targeted electromagnetic response involves exploring large geometry and material spaces. This is a highly iterative process based on trial and error, which is computationally costly and time consuming. Moreover, the non-uniqueness of structural designs and high non-linearity between electromagnetic response and design makes this problem challenging. To model this unintuitive relationship between electromagnetic response and metasurface structural design as a probability distribution in the design space, we introduce a framework for inverse design of nanophotonic metasurfaces based on cyclical deep learning (DL). The proposed framework performs inverse design and optimization mechanism for the generation of meta-atoms and meta-molecules as metasurface units based on DL models and genetic algorithm. The framework includes consecutive DL models that emulate both numerical electromagnetic simulation and iterative processes of optimization, and generate optimized structural designs while simultaneously performing forward and inverse design tasks. A selection and evaluation of generated structural designs is performed by the genetic algorithm to construct a desired optical response and design space that mimics real world responses. Importantly, our cyclical generation framework also explores the space of new metasurface topologies. As an example application of the utility of our proposed architecture, we demonstrate the inverse design of gap-plasmon based half-wave plate metasurface for user-defined optical response. Our proposed technique can be easily generalized for designing nanophtonic metasurfaces for a wide range of targeted optical response.

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

Cited by 10 publications

References 28 publications

A 1.55 μm Wideband 1 × 2 Photonic Power Splitter With Arbitrary Ratio: Characterization and Forward Modeling

A 1.55 μm Wideband 1 × 2 Photonic Power Splitter With Arbitrary Ratio: Characterization and Forward Modeling

Deep Generative Models in Engineering Design: A Review

A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces

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