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Applied Sciences
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Nanophotonic Devices and Technologies
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Nanophotonic Devices and Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 12491

Special Issue Editors

Dr. Raktim Sarma

E-Mail Website
Guest Editor
Center for Integrated Nanotechnologies, Sandia National Laboratories, Albuquerque, NM 87123, USA
Interests: mesoscopic physics; wave propagation in complex media; nanophotonics; optical gyroscopes and sensors; optoelectronics; tunable and nonlinear metamaterials
Dr. Sylvain Gennaro

E-Mail Website
Guest Editor
Center for Integrated Nanotechnologies, Sandia National Laboratories, Albuquerque, NM 87123, USA
Interests: metasurfaces; plasmonics; nonlinear optics

Special Issue Information

Dear Colleagues,

Light–matter interaction at the nanoscale is an extensively studied topic both for fundamental reasons as well as for practical applications ranging from sensing, biomedical imaging, energy harvesting, holography, and new light sources to lasers. The complexity of light–matter interaction in complex media originates from the spatially varying refractive index profile of the photonic medium. The refractive index profile can be spatially uncorrelated for random media, aperiodic, or periodic for metasurfaces and photonic crystals. While random media are extensively studied for applications ranging from biomedical imaging, lasing, to energy harvesting, periodic structures, such as metasurfaces, have made it possible to control light–matter interactions in ways that were not possible before.

In this Special Edition, our focus will be on advances in the areas of controlling light–matter interactions at the nanoscale using complex photonic media such as random media, photonic crystals, metamaterials, and metasurfaces. We are seeking publications in the form of original research articles or review papers addressing recent advancements in the field of light–matter interaction at the nanoscale.

Sincerely,

Dr. Raktim Sarma
Dr. Sylvain Gennaro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Control and study of light transport through random media
  • Studies of employing adaptive wavefront shaping techniques for controlling light
  • transport and for imaging applications in turbid media
  • Photonic and sensing applications using correlated disordered structures
  • Wavefront control, sensing, nonlinear optical effects, and novel optical
  • phenomena using metasurfaces and metamaterials
  • Photonic crystals
  • Topological and non-reciprocal photonics
  • Non-Hermitian Optics and P–T symmetric systems
  • Phase-change materials for photonic applications
  • New materials for tunable photonics
  • Photonics with atomically thin materials

Published Papers (3 papers)

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Research

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13 pages, 3880 KiB  
Article
Classification of Intensity Distributions of Transmission Eigenchannels of Disordered Nanophotonic Structures Using Machine Learning
by Raktim Sarma, Abigail Pribisova, Bjorn Sumner and Jayson Briscoe
Appl. Sci. 2022, 12(13), 6642; https://doi.org/10.3390/app12136642 - 30 Jun 2022
Cited by 2 | Viewed by 1267
Abstract
Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities [...] Read more.
Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities has large random fluctuations due to the interference of multiply scattered electromagnetic waves, even though the statistically averaged spatial profiles of the transmission eigenchannels are universal. Classification of these eigenchannels for a single configuration based on visualization of intensity distributions is difficult. However, successful classification could provide vital information about disordered nanophotonic structures. Emerging methods in machine learning have enabled new investigations into optimized photonic structures. In this work, we combine intensity distributions of the transmission eigenchannels and the transmitted speckle-like intensity patterns to classify the eigenchannels of a single configuration of disordered photonic structures using machine learning techniques. Specifically, we leverage supervised learning methods, such as decision trees and fully connected neural networks, to achieve classification of these transmission eigenchannels based on their intensity distributions with an accuracy greater than 99%, even with a dataset including photonic devices of various disorder strengths. Simultaneous classification of the transmission eigenchannels and the relative disorder strength of the nanophotonic structure is also possible. Our results open new directions for machine learning assisted speckle-based metrology and demonstrate a novel approach to classifying nanophotonic structures based on their electromagnetic field distributions. These insights can be of paramount importance for optimizing light-matter interactions at the nanoscale. Full article
(This article belongs to the Special Issue Nanophotonic Devices and Technologies)
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9 pages, 1614 KiB  
Article
Spectroscopic Ellipsometry and Optical Modelling of Structurally Colored Opaline Thin-Films
by Chris E. Finlayson, Giselle Rosetta and John J. Tomes
Appl. Sci. 2022, 12(10), 4888; https://doi.org/10.3390/app12104888 - 12 May 2022
Cited by 3 | Viewed by 1870
Abstract
The method of spectroscopic ellipsometry is applied to complex periodic nanomaterials, consisting of shear-ordered polymeric nanosphere composites, with intense resonant structural color. A corresponding multilayer optical quasi-model of the system, parametrizing the inherent degree of sample disorder and encompassing key properties of effective [...] Read more.
The method of spectroscopic ellipsometry is applied to complex periodic nanomaterials, consisting of shear-ordered polymeric nanosphere composites, with intense resonant structural color. A corresponding multilayer optical quasi-model of the system, parametrizing the inherent degree of sample disorder and encompassing key properties of effective refractive-index and index-contrast, is developed to elucidate the correlation between the ∆ and Ψ ellipsometric parameters and the shear-induced opaline crystallinity. These approaches offer reliable means of in-line tracking of the sample quality of such “polymer opals” in large scale processing and applications. Full article
(This article belongs to the Special Issue Nanophotonic Devices and Technologies)
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Review

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25 pages, 3705 KiB  
Review
Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks
by Simei Mao, Lirong Cheng, Caiyue Zhao, Faisal Nadeem Khan, Qian Li and H. Y. Fu
Appl. Sci. 2021, 11(9), 3822; https://doi.org/10.3390/app11093822 - 23 Apr 2021
Cited by 42 | Viewed by 8552
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Nanophotonic Devices and Technologies)
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