ArticleLight, science & applications2018
Plasmonic nanostructure design and characterization via Deep Learning.
Article in Light, science & applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
111 citing papers in PubMed.
- Optical Scattering Characteristics of Three-Dimensional Targets Based on Transfer Learning PINNs.Micromachines · 2026Article
- Seeing Beyond RGB Capabilities: Data-Driven and Physics-Guided Broadband Spectral Extrapolation of Plasmonic Nanostructures by Deep Learning.ACS photonics · 2026Article
- MOCLIP: a foundation model for large-scale nanophotonic inverse design.Nature communications · 2026Article
- Broadband Electromagnetic Field Prediction at Unseen Wavelengths via Physics-Guided Neural Operators.Nanophotonics (Berlin, Germany) · 2026Article
- Plasmonic Optical Tweezers and Surface-Enhanced Raman Spectroscopy: Fundamentals, Single-Entity Applications, and the Evolving Role of Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2026Review
- Engineering of Optoelectronic Devices for Renewable Energy Applications.Micromachines · 2026Review
- Angular spectrum-encoded single-shot ultrafast photography.Light, science & applications · 2026Article
- Artificial Intelligence-Driven Metasurfaces Spanning Multidimensional Light Field Control and Free Space Computing.Micromachines · 2026Review
- Machine Learning-Enabled Intelligent Analysis of Surface-Enhanced Raman Scattering: Methods, Applications, and Perspectives.Molecules (Basel, Switzerland) · 2026Review
- Flexible Dual Fabry-Perot Filters Enabling Color Balanced Narrowband Emission Designed by Physics Guided Neural Inverse Optimization.Nanophotonics (Berlin, Germany) · 2026Article
- Nonlocal metasurfaces based on advanced Bayesian learning for satellite communications.Scientific reports · 2026Article
- MxDiffusion: A Physics-Aware Maxwell's Law-Guided Diffusion Model Strategy for Inverse Photonic Metasurface Design.Nano letters · 2026Article
- When Light Drives Discovery: Plasmonics at the Frontier of Energy Materials.Chemistry of materials : a publication of the American Chemical Society · 2026Review
- AI-Assisted Molecular Biosensors: Design Strategies for Wearable and Real-Time Monitoring.International journal of molecular sciences · 2026Review
- Evolutionary Diffusion Framework Empowering High-Performance Freeform Terahertz Metasurface Sensing.Sensors (Basel, Switzerland) · 2026Article
- Ultraprecision, high-capacity, and wide-gamut structural colors enabled by a mixture probability sampling network.Light, science & applications · 2026Article
- Hype vs. Health: How Approved Nanomedicines Have Met (or Missed) Early Predictions.Nanomaterials (Basel, Switzerland) · 2026Article
- Plasmonic imaging of living pancreatic beta-cell networks.Scientific reports · 2026Article
- High-Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning-Augmented Diffusion Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Designing strongly coupled polaritonic structures via statistical machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
51 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light-matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell's equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.
Identifiers
What OpenQuestion holds
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.