ArticleNanophotonics (Berlin, Germany)2022
Deep neural network enabled active metasurface embedded design.
Article in Nanophotonics (Berlin, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Metasurface Vision Transformer: A Generic AI Model for Metasurface Inverse Design.Nanophotonics (Berlin, Germany) · 2026Article
- Review
- Optically readable synaptic modulators based on Tamm plasmon for adaptive multispectral image processing.iScience · 2025Article
- Enhancing metasurface fabricability through minimum feature size enforcement.Nanophotonics (Berlin, Germany) · 2024Article
- Versatile spaceborne photonics with chalcogenide phase-change materials.NPJ microgravity · 2024Review
- Chiral Metasurface Multifocal Lens in the Terahertz Band Based on Deep Learning.Micromachines · 2023Article
- Review: tunable nanophotonic metastructures.Nanophotonics (Berlin, Germany) · 2023Review
- Dual-band optical collimator based on deep-learning designed, fabrication-friendly metasurfaces.Nanophotonics (Berlin, Germany) · 2023Article
- Tunable nanophotonics.Nanophotonics (Berlin, Germany) · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we propose a deep learning approach for forward modeling and inverse design of photonic devices containing embedded active metasurface structures. In particular, we demonstrate that combining neural network design of metasurfaces with scattering matrix-based optimization significantly simplifies the computational overhead while facilitating accurate objective-driven design. As an example, we apply our approach to the design of a continuously tunable bandpass filter in the mid-wave infrared, featuring narrow passband (∼10 nm), high quality factors (
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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.