ReviewNanophotonics (Berlin, Germany)2022
Free-form optimization of nanophotonic devices: from classical methods to deep learning.
Review 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 27 papers.
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Who cites it
27 citing papers in PubMed.
- Three-Dimensional Magneto-Optical Trap Beam Delivery with Scalable Wafer-Level Optics.ACS photonics · 2026Article
- Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning methodology.Scientific reports · 2025Article
- Deep neural networks for inverse design of multimode integrated gratings with simultaneous amplitude and phase control.Nanophotonics (Berlin, Germany) · 2025Article
- Single-gate electro-optic beam switching metasurfaces.Light, science & applications · 2025Article
- Inverse-designed metasurfaces for wavefront restoration in under-display camera systems.Nanophotonics (Berlin, Germany) · 2025Article
- Optical multilayer thin film structure inverse design: From optimization to deep learning.iScience · 2025Review
- Nanophotonic device design based on large language models: multilayer and metasurface examples.Nanophotonics (Berlin, Germany) · 2025Article
- Free-form catenary-inspired meta-couplers for ultra-high or broadband vertical coupling.Nanophotonics (Berlin, Germany) · 2025Article
- Review
- Empowering nanophotonic applications via artificial intelligence: pathways, progress, and prospects.Nanophotonics (Berlin, Germany) · 2025Review
- Recent Advances in Electromagnetic Devices: Design and Optimization.Micromachines · 2025Article
- Multiwavelength Achromatic Deflector in the Visible Using a Single-Layer Freeform Metasurface.Nano letters · 2024Article
- Low-loss and compact arbitrary-order silicon mode converter based on hybrid shape optimization.Nanophotonics (Berlin, Germany) · 2024Article
- Large-scale photonic inverse design: computational challenges and breakthroughs.Nanophotonics (Berlin, Germany) · 2024Review
- Sample-efficient inverse design of freeform nanophotonic devices with physics-informed reinforcement learning.Nanophotonics (Berlin, Germany) · 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
- Unleashing the potential: AI empowered advanced metasurface research.Nanophotonics (Berlin, Germany) · 2024Review
- Stochastic and multi-objective design of photonic devices with machine learning.Scientific reports · 2024Article
- Designing Metasurfaces for Efficient Solar Energy Conversion.ACS photonics · 2023Review
- A Quick Method for Predicting Reflectance Spectra of Nanophotonic Devices via Artificial Neural Network.Nanomaterials (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanophotonic devices have enabled microscopic control of light with an unprecedented spatial resolution by employing subwavelength optical elements that can strongly interact with incident waves. However, to date, most nanophotonic devices have been designed based on fixed-shape optical elements, and a large portion of their design potential has remained unexplored. It is only recently that free-form design schemes have been spotlighted in nanophotonics, offering routes to make a break from conventional design constraints and utilize the full design potential. In this review, we systematically overview the nascent yet rapidly growing field of free-form nanophotonic device design. We attempt to define the term "free-form" in the context of photonic device design, and survey different strategies for free-form optimization of nanophotonic devices spanning from classical methods, adjoint-based methods, to contemporary machine-learning-based approaches.
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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.