ReviewNanoscale advances2020
Deep learning: a new tool for photonic nanostructure design.
Review in Nanoscale advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Photonic Nanomaterials for Wearable Health Solutions.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Machine learning enhanced design and knowledge discovery for multi-junction photonic power converters.Scientific reports · 2025Article
- Programmable photonic unitary circuits for light computing.Nanophotonics (Berlin, Germany) · 2025Review
- Optical multilayer thin film structure inverse design: From optimization to deep learning.iScience · 2025Review
- Enabling Fast AI-Driven Inverse Design of a Multifunctional Nanosurface by Parallel Evolution Strategies.Nanomaterials (Basel, Switzerland) · 2024Article
- Nested deep transfer learning for modeling of multilayer thin films.Advanced photonics · 2024Article
- Advancing materials science through next-generation machine learning.Current opinion in solid state & materials science · 2024Article
- Schrödinger's Red Beyond 65,000 Pixel-Per-Inch by Multipolar Interaction in Freeform Meta-Atom through Efficient Neural Optimizer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Article
- A newcomer's guide to deep learning for inverse design in nano-photonics.Nanophotonics (Berlin, Germany) · 2023Review
- Research Progress in Surface-Enhanced Infrared Absorption Spectroscopy: From Performance Optimization, Sensing Applications, to System Integration.Nanomaterials (Basel, Switzerland) · 2023Review
- A deep neural network for general scattering matrix.Nanophotonics (Berlin, Germany) · 2023Article
- Transfer Learning for Modeling Plasmonic Nanowire Waveguides.Nanomaterials (Basel, Switzerland) · 2022Article
- Machine Learning in Interpolation and Extrapolation for Nanophotonic Inverse Design.ACS omega · 2022Article
- Deep learning in light-matter interactions.Nanophotonics (Berlin, Germany) · 2022Review
- Inverse design of structural color: finding multiple solutionsNanophotonics (Berlin, Germany) · 2022Article
- Advancing statistical learning and artificial intelligence in nanophotonics inverse design.Nanophotonics (Berlin, Germany) · 2022Review
- A learning based approach for designing extended unit cell metagratings.Nanophotonics (Berlin, Germany) · 2022Article
- A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors.Nanophotonics (Berlin, Germany) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early results have shown the potential of Deep Learning (DL) to disrupt the fields of optical inverse-design, particularly, the inverse design of nanostructures. In the last three years, the complexity of the optical nanostructure being designed and the sophistication of the employed DL methodology have steadily increased. This topical review comprehensively surveys DL based design examples from the nanophotonics literature. Notwithstanding the early success of this approach, its limitations, range of validity and its place among established design techniques remain to be assessed. The review also provides a perspective on the limitations of this approach and emerging research directions. It is hoped that this topical review may help readers to identify unaddressed problems, to choose an initial setup for a specific problem, and, to identify means to improve the performance of existing DL based workflows.
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.