ArticleNanophotonics (Berlin, Germany)2019
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale.
Article in Nanophotonics (Berlin, Germany), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
56 citing papers in PubMed.
- Optical Scattering Characteristics of Three-Dimensional Targets Based on Transfer Learning PINNs.Micromachines · 2026Article
- Real-Time Probing of Molecular Affinity Using Optical Tweezers.Sensors (Basel, Switzerland) · 2026Article
- Inverse design and 3D printing of a multiport microwave power splitter: a scalable electromagnetic design framework.Communications engineering · 2026Article
- Machine learning enhanced design and knowledge discovery for multi-junction photonic power converters.Scientific reports · 2025Article
- Nearly Full-Stokes Polarization Control Enabled by Geometric Polarization in Broadband Metasurfaces.Nano letters · 2025Article
- Fiber-based angular demultiplexer using nanoprinted periodic structures on single-mode multicore fibers.Nature communications · 2025Article
- Empowering nanophotonic applications via artificial intelligence: pathways, progress, and prospects.Nanophotonics (Berlin, Germany) · 2025Review
- Predicting and synthesizing terahertz spoof surface plasmon polariton devices with a convolutional neural network model.Scientific reports · 2025Article
- Chirality in nanomaterials.Scientific reports · 2024Article
- Nested deep transfer learning for modeling of multilayer thin films.Advanced photonics · 2024Article
- Localized Plasmonic Structured Illumination Microscopy Using Hybrid Inverse Design.Nano letters · 2024Article
- Review
- Non-interleaved chiral metasurfaces and neural networks enhance the spatial resolution of polarimetry.Light, science & applications · 2024Article
- Designing Metasurfaces for Efficient Solar Energy Conversion.ACS photonics · 2023Review
- Deep neural network-based phase calibration in integrated optical phased arrays.Scientific reports · 2023Article
- Switching on Versatility: Recent Advances in Switchable Plasmonic Nanostructures.Small science · 2023Article
- Machine learning to optimize additive manufacturing for visible photonics.Nanophotonics (Berlin, Germany) · 2023Article
- Super-compact universal quantum logic gates with inverse-designed elements.Science advances · 2023Article
- Deep Learning Aided Neuroimaging and Brain Regulation.Sensors (Basel, Switzerland) · 2023Review
- Back-propagation-assisted inverse design of structured light fields for given profiles of optical force.Nanophotonics (Berlin, Germany) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.