ArticleScientific reports2019
Double-deep Q-learning to increase the efficiency of metasurface holograms.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects.Nanophotonics (Berlin, Germany) · 2025Review
- A perspective on the artificial intelligence's transformative role in advancing diffractive optics.iScience · 2024Review
- Deep Learning-Based Design Method for Acoustic Metasurface Dual-Feature Fusion.Materials (Basel, Switzerland) · 2024Article
- Unleashing the potential: AI empowered advanced metasurface research.Nanophotonics (Berlin, Germany) · 2024Review
- Rational design of arbitrary topology in three-dimensional spaceNanophotonics (Berlin, Germany) · 2024Article
- A Design Method of Diffraction Structure Based on Metasurface for High-Resolution Spectroscopy.Nanomaterials (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Meta-optics.Chemical reviews · 2022Review
- Deep neural learning based optimization for automated high performance antenna designs.Scientific reports · 2022Article
- Free-form optimization of nanophotonic devices: from classical methods to deep learning.Nanophotonics (Berlin, Germany) · 2022Review
- Optical meta-waveguides for integrated photonics and beyond.Light, science & applications · 2021Review
- Deep neural network-based automatic metasurface design with a wide frequency range.Scientific reports · 2021Article
- Design and verify a dual modulated metasurface in terahertz range.Scientific reports · 2020Article
- Deep Q-network to produce polarization-independent perfect solar absorbers: a statistical report.Nano convergence · 2020Article
- Scalable and High-Throughput Top-Down Manufacturing of Optical Metasurfaces.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We use a double deep Q-learning network (DDQN) to find the right material type and the optimal geometrical design for metasurface holograms to reach high efficiency. The DDQN acts like an intelligent sweep and could identify the optimal results in ~5.7 billion states after only 2169 steps. The optimal results were found between 23 different material types and various geometrical properties for a three-layer structure. The computed transmission efficiency was 32% for high-quality metasurface holograms; this is two times bigger than the previously reported results under the same conditions. The found structure is transmission-type and polarization-independent and works in the visible region.
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Registered trials
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