Three-dimensional vectorial holography based on machine learning inverse design

Ren, Haoran; Shao, Wei; Li, Yang; Salim, Flora D.; Gu, Miṅ

doi:10.1126/sciadv.aaz4261

Cited by 108 publications

(69 citation statements)

References 40 publications

(59 reference statements)

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“…We remark that our design method can directly handle arbitrary field distributions, including the 3D and vectorial forms that are demonstrated in two recent works. [ 48,49 ]…”

Section: Resultsmentioning

confidence: 99%

Full‐State Synthesis of Electromagnetic Fields using High Efficiency Phase‐Only Metasurfaces

Wang

Fang

et al. 2020

Adv Funct Materials

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A metasurface is a thin array of subwavelength elements with designable scattering responses, and metasurface holography is a powerful tool for imaging and field control. The existing metasurface holograms are classified into two types: one is based on phase-only metasurfaces (including the recently presented vectorial metasurface holography), which has high power efficiency but cannot control the phases of generated fields; while the other is based on phase-amplitude-modulated metasurfaces, which can control both field amplitudes and phases in the region of interest (ROI) but has very low efficiency. Here, for the first time, it is proposed to synthesize the field amplitudes and phases in ROI simultaneously and independently by using high-efficiency phase-only metasurfaces. All points in ROI may have independent values of field amplitudes and phases, and the requirements for X and Y components may be different in achieving spatially varied polarization states. To this end, an efficient design method based on equivalent electromagnetic model and gradient-based nonlinear optimization is proposed. Full-wave simulations and experimental results demonstrate that the phase-only metasurface designed by the method has 10 times higher efficiency than the phase-amplitude-modulated metasurface. This work opens a way to realize more complicated and high-efficiency metasurface holography.

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“…We remark that our design method can directly handle arbitrary field distributions, including the 3D and vectorial forms that are demonstrated in two recent works. [ 48,49 ]…”

Section: Resultsmentioning

confidence: 99%

Full‐State Synthesis of Electromagnetic Fields using High Efficiency Phase‐Only Metasurfaces

Wang

Fang

et al. 2020

Adv Funct Materials

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“…Although conventional structural design methods, including physics‐based approaches and numerical simulations, offer important guidelines, it is not trivial to find the right structures with ideal selective emission spectra. We envisage simple photonic materials and structures designed and optimized by advanced methods such as the inverse design methods, [ 64–75 ] which enable nonintuitive and irregularly shaped structures, outperforming physically or empirically designed structures. Particularly, the artificial neural networks, as one of the most powerful machine learning methods, have shown orders of magnitude faster and much accuracy in optimizing structures in high dimensional space.…”

Section: Discussionmentioning

confidence: 99%

Photonics Empowered Passive Radiative Cooling

Zhang

Chu

Cai

et al. 2021

Advanced Photonics Research

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Passive radiative cooling has recently received renewed interest because of its unprecedented capabilities in cooling terrestrial objects below ambient air temperature without external energy consumption and greenhouse gas emission. This technology has been demonstrated as promising as replacements/complements of conventional compressed air‐based active cooling systems, which can significantly impact the global energy landscape by providing a green and efficient cooling way. The key to this success is judiciously designed photonic micro/nanostructures, which simultaneously reflect solar irradiation and emit thermal infrared emission across the atmospheric transparency window 8–13 μm. Herein, an introduction of the fundamental principles of passive radiative cooling is given, discussing the critical factors associated with the net cooling power of radiative cooling. Following this, the recently emerged photonic materials and structures (e.g., multilayer thin films, micro/nanoparticles, photonic crystals, metamaterials, metasurfaces, etc.) that facilitate radiative cooling are reviewed and fruitfully analyzed and discussed. Some possible scale‐up manufacturing ways toward the practical deployment of this energy‐efficient technology in real‐world applications are then discussed. The potential applications are also summarized and envisioned. Finally, perspectives on the future development in conjunction with artificial intelligent design of photonic structures and materials are presented and discussed.

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“…The ability to design on demand meta-atom geometries with the specified phase and amplitude responses enables various striking applications, including multi-focal metalenses, [10] beam deflectors, [58] holograms, [3,59] and airy beam generators. [10,12] Using the trained network, we designed a bifocal metalens operating at 50 THz, which requires simultaneous amplitude and phase modulations of each meta-atom within the metalens.…”

Section: Doi: 101002/adom202001433mentioning

confidence: 99%

Multifunctional Metasurface Design with a Generative Adversarial Network

Zheng

Tang

et al. 2021

Advanced Optical Materials

105

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Metasurfaces have enabled precise electromagnetic (EM) wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices. These advantages in precision and functionality come at the cost of tremendous difficulty in finding individual meta‐atom structures based on specific requirements (commonly formulated in terms of EM responses), which makes the design of multifunctional metasurfaces a key challenge in this field. In this paper, a generative adversarial network that can tackle this problem and generate meta‐atom/metasurface designs to meet multifunctional design goals is presented. Unlike conventional trial‐and‐error or iterative optimization design methods, this new methodology produces on‐demand free‐form structures involving only a single design iteration. More importantly, the network structure and the robust training process are independent of the complexity of design objectives, making this approach ideal for multifunctional device design. Additionally, the ability of the network to generate distinct classes of structures with similar EM responses but different physical features can provide added latitude to accommodate other considerations such as fabrication constraints and tolerances. The network's ability to produce a variety of multifunctional metasurface designs is demonstrated by presenting a bifocal metalens, a polarization‐multiplexed beam deflector, a polarization‐multiplexed metalens, and a polarization‐independent metalens.

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Three-dimensional vectorial holography based on machine learning inverse design

Cited by 108 publications

References 40 publications

Full‐State Synthesis of Electromagnetic Fields using High Efficiency Phase‐Only Metasurfaces

Full‐State Synthesis of Electromagnetic Fields using High Efficiency Phase‐Only Metasurfaces

Photonics Empowered Passive Radiative Cooling

Multifunctional Metasurface Design with a Generative Adversarial Network

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