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

Exploring Trends and Opportunities in Quantum‐Enhanced Advanced Photonic Illumination Technologies DOI
Bakr Ahmed Taha, Ali J. Addie, Adawiya J. Haider

et al.

Advanced Quantum Technologies, Journal Year: 2024, Volume and Issue: 7(3)

Published: Jan. 18, 2024

Abstract The development of quantum‐enabled photonic technologies has opened new avenues for advanced illumination across diverse fields, including sensing, computing, materials, and integration. This review highlights how Quantum‐enhanced sensing imaging exploit nonclassical correlations to attain unprecedented accuracy in chaotic environments. As well as guaranteeing secure communications, quantum cryptography, protected by physical principles, ensures unbreakable cryptographic key exchange. computing speed increases exponentially, previously unimplementable uses classical computers become feasible. On‐chip integration enables the mass production components pervasive applications facilitating miniaturization scalability. A powerful flexible platform is produced when systems are combined. Quantum spin liquids other topological materials can maintain their states while subject decoherence. Despite challenges with decoherence, production, commercialization, photonics an exciting area study that promises lighting techniques impossible conventional optics. To realize this promise, researchers from several fields must work together solve complex technical problems decode fundamental physics. Finally, advances have potential evolve devices cutting‐edge methods usher a age options based on dots.

Language: Английский

Citations

27
Electrically tunable optical metasurfaces DOI Creative Commons
Fei Ding, Chao Meng, Sergey I. Bozhevolnyi

et al.

Photonics Insights, Journal Year: 2024, Volume and Issue: 3(3), P. R07 - R07

Published: Jan. 1, 2024

Citations

20
A deep generative multiscale topology optimization framework considering manufacturing defects and parametrical uncertainties DOI

Yi-Chen Wu,

Lei Wang,

Zeshang Li

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 437, P. 117778 - 117778

Published: Jan. 22, 2025

Language: Английский

Citations

6
Electromagnetic metamaterial agent DOI Creative Commons

Shengguo Hu,

Ming‐Yi Li, Jiawen Xu

et al.

Light Science & Applications, Journal Year: 2025, Volume and Issue: 14(1)

Published: Jan. 1, 2025

Abstract Metamaterials have revolutionized wave control; in the last two decades, they evolved from passive devices via programmable to sensor-endowed self-adaptive realizing a user-specified functionality. Although deep-learning techniques play an increasingly important role metamaterial inverse design, measurement post-processing and end-to-end optimization, their is ultimately still limited approximating specific mathematical relations; serving as proxy of human operator, predefined Here, we propose experimentally prototype paradigm shift toward agent (coined metaAgent) endowed with reasoning cognitive capabilities enabling autonomous planning successful execution diverse long-horizon tasks, including electromagnetic (EM) field manipulations interactions robots humans. Leveraging recently released foundation models, metaAgent reasons high-level natural language, acting upon prompts evolving complex environment. Specifically, metaAgent’s cerebrum performs task language multi-agent discussion mechanism, where agents are domain experts sensing, planning, grounding, coding. In response live environmental feedback within real-world setting emulating ambient-assisted living context (including requests language), our self-organizes hierarchy EM manipulation tasks conjunction commanding robot. masters foundational skills related wireless communications it memorizes learns past experience based on feedback.

Language: Английский

Citations

3
Progress on intelligent metasurfaces for signal relay, transmitter, and processor DOI Creative Commons
Chao Qian, Longwei Tian, Hongsheng Chen

et al.

Light Science & Applications, Journal Year: 2025, Volume and Issue: 14(1)

Published: Feb. 25, 2025

Abstract Pursuing higher data rate with limited spectral resources is a longstanding topic that has triggered the fast growth of modern wireless communication techniques. However, massive deployment active nodes to compensate for propagation loss necessitates high hardware expenditure, energy consumption, and maintenance cost, as well complicated network interference issues. Intelligent metasurfaces, composed number subwavelength passive or meta-atoms, have recently found be new paradigm actively reshape environment in green way, distinct from conventional works passively adapt surrounding. In this review, we offer unified perspective on how intelligent metasurfaces can facilitate three manners: signal relay, transmitter, processor. We start by basic modeling channel evolution passive, metasurfaces. Integrated various deep learning algorithms, cater ever-changing environments without human intervention. Then, overview specific experimental advancements using conclude identifying key issues practical implementations surveying directions, such gain knowledge migration.

Language: Английский

Citations

2
Intelligent designs in nanophotonics: from optimization towards inverse creation DOI Creative Commons
Ning Wang, Wei Yan, Yurui Qu

et al.

PhotoniX, Journal Year: 2021, Volume and Issue: 2(1)

Published: Oct. 23, 2021

Abstract Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years. Inverse design of functional nanostructures is at heart this topic, in which artificial (AI) furnishes various optimization toolboxes speed up prototyping photonic layouts with enhanced performance. In review, we offer systemic view on recent advancements nanophotonic components designed by algorithms, manifesting development trend from performance optimizations towards inverse creations novel designs. To illustrate interplays between two fields, AI photonics, take meta-atom spectral manipulation as case study introduce algorithm operational principles, subsequently review their manifold usages among set popular meta-elements. As arranged levels individual optimized piece practical system, discuss algorithm-assisted designs examine mutual benefits. We further comment open questions including reasonable applications advanced expensive data issue, benchmarking, etc. Overall, envision mounting photonic-targeted methodologies substantially push forward profit both fields.

Language: Английский

Citations

73
Benchmarking deep learning-based models on nanophotonic inverse design problems DOI

Taigao Ma,

Mustafa Tobah,

Haozhu Wang

et al.

Opto-Electronic Science, Journal Year: 2022, Volume and Issue: 1(1), P. 210012 - 210012

Published: Jan. 1, 2022

Photonic inverse design concerns the problem of finding photonic structures with target optical properties. However, traditional methods based on optimization algorithms are time-consuming and computationally expensive. Recently, deep learning-based approaches have been developed to tackle efficiently. Although most these neural network models demonstrated high accuracy in different problems, no previous study has examined potential effects under given constraints nanomanufacturing. Additionally, relative strength not fully investigated. Here, we benchmark three commonly used learning design: Tandem networks, Variational Auto-Encoders, Generative Adversarial Networks. We provide detailed comparisons terms their accuracy, diversity, robustness. find that tandem networks Auto-Encoders give best while Networks lead diverse predictions. Our findings could serve as a guideline for researchers select model can suit criteria fabrication considerations. In addition, our code data publicly available, which be future development benchmarking.

Language: Английский

Citations

67
Machine learning assisted design of shape-programmable 3D kirigami metamaterials DOI Creative Commons
Nicolas A. Alderete, Nibir Pathak, Horacio D. Espinosa

et al.

npj Computational Materials, Journal Year: 2022, Volume and Issue: 8(1)

Published: Sept. 6, 2022

Abstract Kirigami-engineering has become an avenue for realizing multifunctional metamaterials that tap into the instability landscape of planar surfaces embedded with cuts. Recently, it been shown two-dimensional Kirigami motifs can unfurl a rich space out-of-plane deformations, which are programmable and controllable across spatial scales. Notwithstanding Kirigami’s versatility, arriving at cut layout yields desired functionality remains challenge. Here, we introduce comprehensive machine learning framework to shed light on design rationally guide control Kirigami-based materials from meta-atom metamaterial level. We employ combination clustering, tandem neural networks, symbolic regression analyses obtain fulfills specific constraints inform their deployment. Our systematic approach is experimentally demonstrated by examining variety applications different hierarchical levels, effectively providing tool discovery shape-shifting metamaterials.

Language: Английский

Citations

67
Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator DOI
Yuxiao Zhu, Daniel W. Newbrook, Peng Dai

et al.

Applied Energy, Journal Year: 2021, Volume and Issue: 305, P. 117800 - 117800

Published: Sept. 27, 2021

Language: Английский

Citations

65
Dispersion relation prediction and structure inverse design of elastic metamaterials via deep learning DOI Open Access
Weifeng Jiang, Yangyang Zhu, Guofu Yin

et al.

Materials Today Physics, Journal Year: 2022, Volume and Issue: 22, P. 100616 - 100616

Published: Jan. 1, 2022

Language: Английский

Citations

55