Diffractive optical elements 75 years on: from micro-optics to metasurfaces DOI Creative Commons
Qiang Zhang, Zehao He, Zhenwei Xie

et al.

Photonics Insights, Journal Year: 2023, Volume and Issue: 2(4), P. R09 - R09

Published: Jan. 1, 2023

Diffractive optical elements (DOEs) are intricately designed devices with the purpose of manipulating light fields by precisely modifying their wavefronts. The concept DOEs has its origins dating back to 1948 when D. Gabor first introduced holography. Subsequently, researchers binary (BOEs), including computer-generated holograms (CGHs), as a distinct category within realm DOEs. This was revolution in devices. next major breakthrough field manipulation occurred during early 21st century, marked advent metamaterials and metasurfaces. Metasurfaces particularly appealing due ultra-thin, ultra-compact properties capacity exert precise control over virtually every aspect fields, amplitude, phase, polarization, wavelength/frequency, angular momentum, etc. advancement micro/nano-structures also enabled various applications such information acquisition, transmission, storage, processing, display. In this review, we cover fundamental science, cutting-edge technologies, wide-ranging associated micro/nano-scale for regulating fields. We delve into prevailing challenges pursuit developing viable technology real-world applications. Furthermore, offer insights potential future research trends directions manipulation.

Language: Английский

Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next DOI Creative Commons
Salvatore Cuomo,

Vincenzo Schiano Di Cola,

Fabio Giampaolo

et al.

Journal of Scientific Computing, Journal Year: 2022, Volume and Issue: 92(3)

Published: July 26, 2022

Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the network itself. PINNs nowadays used to solve PDEs, fractional integral-differential and stochastic PDEs. This novel methodology has arisen multi-task learning framework in which NN must fit observed data while reducing PDE residual. article provides comprehensive review literature on PINNs: primary goal study was characterize these their related advantages disadvantages. The also attempts incorporate publications broader range collocation-based physics informed networks, stars form vanilla PINN, well many other variants, such physics-constrained (PCNN), variational hp-VPINN, conservative PINN (CPINN). indicates most research focused customizing through different activation functions, gradient optimization techniques, structures, loss function structures. Despite wide applications for have been used, by demonstrating ability be more feasible some contexts than classical numerical techniques Finite Element Method (FEM), advancements still possible, notably theoretical issues remain unresolved.

Language: Английский

Citations

1083
Optical meta-waveguides for integrated photonics and beyond DOI Creative Commons
Yuan Meng, Yizhen Chen,

Longhui Lu

et al.

Light Science & Applications, Journal Year: 2021, Volume and Issue: 10(1)

Published: Nov. 22, 2021

Abstract The growing maturity of nanofabrication has ushered massive sophisticated optical structures available on a photonic chip. integration subwavelength-structured metasurfaces and metamaterials the canonical building block waveguides is gradually reshaping landscape integrated circuits, giving rise to numerous meta-waveguides with unprecedented strength in controlling guided electromagnetic waves. Here, we review recent advances meta-structured that synergize various functional subwavelength architectures diverse waveguide platforms, such as dielectric or plasmonic fibers. Foundational results representative applications are comprehensively summarized. Brief physical models explicit design tutorials, either intuition-based methods computer algorithms-based inverse designs, cataloged well. We highlight how meta-optics can infuse new degrees freedom waveguide-based devices systems, by enhancing light-matter interaction drastically boost device performance, offering versatile designer media for manipulating light nanoscale enable novel functionalities. further discuss current challenges outline emerging opportunities this vibrant field biomedical sensing, artificial intelligence beyond.

Language: Английский

Citations

311
Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review DOI
Omar Khatib, Simiao Ren, Jordan M. Malof

et al.

Advanced Functional Materials, Journal Year: 2021, Volume and Issue: 31(31)

Published: May 28, 2021

Abstract Deep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and driving scientific discovery. Artificial electromagnetic materials (AEMs)—including metamaterials, photonic crystals, plasmonics—are fields where DNN results valorize the data driven approach; especially in cases conventional methods failed. In view of great potential deep learning for future artificial research, status field with a focus on recent advances, key limitations, directions is reviewed. Strategies, guidance, evaluation, limits using both forward inverse AEM problems presented.

Language: Английский

Citations

130
Fiber laser development enabled by machine learning: review and prospect DOI Creative Commons
Min Jiang, Hanshuo Wu, Yi An

et al.

PhotoniX, Journal Year: 2022, Volume and Issue: 3(1)

Published: July 13, 2022

Abstract In recent years, machine learning, especially various deep neural networks, as an emerging technique for data analysis and processing, has brought novel insights into the development of fiber lasers, in particular complex, dynamical, or disturbance-sensitive laser systems. This paper highlights attractive research that adopted learning field, including design manipulation on-demand output, prediction control nonlinear effects, reconstruction evaluation properties, well robust lasers We also comment on challenges potential future development.

Language: Английский

Citations

87
Computational spectrometers enabled by nanophotonics and deep learning DOI Creative Commons
Li Gao, Yurui Qu, Lianhui Wang

et al.

Nanophotonics, Journal Year: 2022, Volume and Issue: 11(11), P. 2507 - 2529

Published: Jan. 24, 2022

Abstract A new type of spectrometer that heavily relies on computational technique to recover spectral information is introduced. They are different from conventional optical spectrometers in many important aspects. Traditional offer high resolution and wide range, but they so bulky expensive as be difficult deploy broadly the field. Emerging applications machine sensing imaging require low-cost miniaturized specifically designed for certain applications. Computational well suited these generally low cost single-shot operation, with adequate spatial resolution. The combines recent progress nanophotonics, advanced signal processing learning. Here we review spectrometers, identify key challenges, note directions likely develop near future.

Language: Английский

Citations

85
Structural color generation: from layered thin films to optical metasurfaces DOI Creative Commons

Danyan Wang,

Zeyang Liu, Haozhu Wang

et al.

Nanophotonics, Journal Year: 2023, Volume and Issue: 12(6), P. 1019 - 1081

Published: Feb. 21, 2023

Recent years have witnessed a rapid development in the field of structural coloration, colors generated from interaction nanostructures with light. Compared to conventional color generation based on pigments and dyes, exhibits unique advantages terms spatial resolution, operational stability, environmental friendliness, multiple functionality. Here, we discuss recent coloration layered thin films optical metasurfaces. This review first presents fundamentals science introduces few popular spaces used for evaluation. Then, it elaborates representative physical mechanisms generation, including Fabry-Pérot resonance, photonic crystal guided mode plasmon Mie resonance. Optimization methods efficient structure parameter searching, fabrication techniques large-scale low-cost manufacturing, as well device designs dynamic displaying are discussed subsequently. In end, surveys diverse applications various areas such printing, sensing, advanced photovoltaics.

Language: Английский

Citations

84
Multiple and spectrally robust photonic magic angles in reconfigurable α-MoO3 trilayers DOI
Jiahua Duan, Gonzalo Álvarez‐Pérez, C. Lanza

et al.

Nature Materials, Journal Year: 2023, Volume and Issue: 22(7), P. 867 - 872

Published: June 22, 2023

Language: Английский

Citations

52
Roadmap on photonic metasurfaces DOI
Sebastian A. Schulz, Rupert F. Oulton, Mitchell Kenney

et al.

Applied Physics Letters, Journal Year: 2024, Volume and Issue: 124(26)

Published: June 24, 2024

Here we present a roadmap on Photonic metasurfaces. This document consists of number perspective articles different applications, challenge areas or technologies underlying photonic Each will introduce the topic, state art as well give an insight into future direction subfield.

Language: Английский

Citations

44
Machine Learning Aided Design and Optimization of Thermal Metamaterials DOI Creative Commons

Changliang Zhu,

Emmanuel Anuoluwa Bamidele, Xiangying Shen

et al.

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(7), P. 4258 - 4331

Published: March 28, 2024

Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) been able to predict some unprecedented thermal properties. In this review, we first elucidate methodologies underpinning discriminative and generative models, as well paradigm of optimization approaches. Then, present a series case studies showcasing application in metamaterial design. Finally, give brief discussion on challenges opportunities fast developing field. particular, review provides: (1) Optimization metamaterials using algorithms achieve specific target (2) Integration models with enhance computational efficiency. (3) Generative structural design metamaterials.

Language: Английский

Citations

37
Quantitative phase imaging based on holography: trends and new perspectives DOI Creative Commons

Zhengzhong Huang,

Liangcai Cao

Light Science & Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: June 27, 2024

Abstract In 1948, Dennis Gabor proposed the concept of holography, providing a pioneering solution to quantitative description optical wavefront. After 75 years development, holographic imaging has become powerful tool for wavefront measurement and phase imaging. The emergence this technology given fresh energy physics, biology, materials science. Digital holography (DH) possesses advantages wide-field, non-contact, precise, dynamic capability complex-waves. DH unique capabilities propagation fields by measuring light scattering with information. It offers visualization refractive index thickness distribution weak absorption samples, which plays vital role in pathophysiology various diseases characterization materials. provides possibility bridge gap between disciplines. is described complex amplitude. complex-value complex-domain reconstructed from intensity-value camera real-domain. Here, we regard process recording reconstruction as transformation real-domain, discuss mathematics physical principles reconstruction. We review underlying principles, technical approaches, breadth applications. conclude emerging challenges opportunities based on combining other methodologies that expand scope utility even further. multidisciplinary nature brings application experts together label-free cell analytical chemistry, clinical sciences, sensing, semiconductor production.

Language: Английский

Citations

33