Global Inverse Design across Multiple Photonic Structure Classes Using Generative Deep Learning (Advanced Optical Materials 20/2021)

Yeung, Christopher; Tsai, Ryan; Pham, Benjamin; King, Brian; Kawagoe, Yusaku; Ho, David; Liang, Julia; Knight, Mark W.; Raman, Aaswath P.

doi:10.1002/adom.202170079

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…155,156 Furthermore, other successful techniques from various fields, such as Tandem network, 24 reinforcement learning 29 and generative model. 28,157 These techniques have demonstrated success in other domains and offer promising avenues for advancing the field of integrated photonic device design through inverse design methodologies.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, generative models, such as generative adversarial networks (GAN), have been introduced to generate counter-intuitive irregular devices that exhibit exceptional performance. 28 Due to the rapid development of DNNs in other fields, many state-of-art networks such as Reinforcement learning have been increasingly employed in the inverse design of integrated photonic devices, showcasing significant potential for achieving high-performance designs. 29 All methods mentioned have been employed for designing different kinds of integrated photonic devices, which can be categorized into three structures implementations as illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recent progress on inverse design for integrated photonic devices: methodology and applications

Shen,

Hong,

Ren

et al. 2024

J. Nanophoton.

View full text Add to dashboard Cite

Photonic integrated circuits (PICs) have attracted great attention as promising platforms for high-data-rate communications and high-performance computing. For the PICs, photonic devices with compatible materials, compact footprint, high-performance, and sophisticated functionalities are necessary building blocks. Design optimization to implement such devices for target applications and requirements are of critical importance. In this respect, inverse design methods, including iterative optimizations and deep neural networks, have demonstrated significant advantages over the traditional simulation-based trial-and-error optimization approach. We provide an overview of the recent progress on the inverse designs for the integrated photonic devices. The principles and procedure of the inverse design methods are presented and discussed, followed by a summary of the methods employed for specific integrated photonic devices in different integrated photonics material platforms. Finally, topics of future applications and fabrication constraints for the inverse design methods are discussed.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recent progress on inverse design for integrated photonic devices: methodology and applications

Shen,

Hong,

Ren

et al. 2024

J. Nanophoton.

View full text Add to dashboard Cite

show abstract

Design of Phononic Bandgap Metamaterials Based on Gaussian Mixture Beta Variational Autoencoder and Iterative Model Updating

Wang

Xian

Baccouche

et al. 2022

Journal of Mechanical Design

View full text Add to dashboard Cite

Phononic bandgap metamaterials, which consist of periodic cellular structures, are capable of absorbing energy within a certain frequency range. Designing metamaterials that trap waves across a wide wave frequency range is still a challenging task. In this paper, we present a deep feature learning-based design framework for both unsupervised generative design and supervised learning-based exploitative optimization. The Gaussian mixture beta variational autoencoder (GM-βVAE) is employed to extract latent features as design variables. Gaussian Process (GP) regression models are trained to predict the relationship between latent features and the properties for the property-driven optimization. The optimal structural designs are reconstructed by mapping the optimized latent feature values to the original image space. Compared with the regular variational autoencoder (VAE), we demonstrate that GM-βVAE has better learning capability and is able to generate a more diversified design set in unsupervised generative design. Furthermore, we propose an iterative GM-βVAE model updating-based design framework. In each iteration, the optimal designs found property-driven optimization are used to update the training dataset. The GM-βVAE model is re-trained with the updated dataset for the optimization search in the next iteration. A comparative study between the traditional single-loop and the iterative approaches is presented to demonstrate the effectiveness of the iterative framework. The caveats to designing phonic bandgap metamaterials are summarized.

show abstract

Hybrid supervised and reinforcement learning for the design and optimization of nanophotonic structures

Yeung,

Pham,

Zhang

et al. 2024

Opt. Express

View full text Add to dashboard Cite

From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of nanophotonic circuits and components. However, both data-driven and exploration-based machine learning strategies have limitations in their effectiveness for nanophotonic inverse design. Supervised machine learning approaches require large quantities of training data to produce high-performance models and have difficulty generalizing beyond training data given the complexity of the design space. Unsupervised and reinforcement learning-based approaches on the other hand can have very lengthy training or optimization times associated with them. Here we demonstrate a hybrid supervised learning and reinforcement learning approach to the inverse design of nanophotonic structures and show this approach can reduce training data dependence, improve the generalizability of model predictions, and significantly shorten exploratory training times. The presented strategy thus addresses several contemporary deep learning-based challenges, while opening the door for new design methodologies that leverage multiple classes of machine learning algorithms to produce more effective and practical solutions for photonic design.

show abstract

Global Inverse Design across Multiple Photonic Structure Classes Using Generative Deep Learning (Advanced Optical Materials 20/2021)

Cited by 5 publications

References 0 publications

Recent progress on inverse design for integrated photonic devices: methodology and applications

Recent progress on inverse design for integrated photonic devices: methodology and applications

Design of Phononic Bandgap Metamaterials Based on Gaussian Mixture Beta Variational Autoencoder and Iterative Model Updating

Hybrid supervised and reinforcement learning for the design and optimization of nanophotonic structures

Contact Info

Product

Resources

About