ArticleNature nanotechnology2019
Pushing the limits of optical information storage using deep learning.
Article in Nature nanotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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Who cites it
19 citing papers in PubMed.
- MOCLIP: a foundation model for large-scale nanophotonic inverse design.Nature communications · 2026Article
- Angle-resolved scatterometry combined with deep learning assisted in-situ monitoring of nanowire doping in molecular beam epitaxy process.Microsystems & nanoengineering · 2025Article
- Emerging trends in SERS-based veterinary drug detection: multifunctional substrates and intelligent data approaches.NPJ science of food · 2025Review
- Programmable electron-induced color router array.Light, science & applications · 2025Article
- A guidance to intelligent metamaterials and metamaterials intelligence.Nature communications · 2025Review
- Holographic multiplexing metasurface with twisted diffractive neural network.Nature communications · 2024Article
- Artificial Intelligence-Enhanced Metasurfaces for Instantaneous Measurements of Dispersive Refractive Index.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Snapshot computational spectroscopy enabled by deep learning.Nanophotonics (Berlin, Germany) · 2024Article
- Perovskite single-pixel detector for dual-color metasurface imaging recognition in complex environment.Light, science & applications · 2023Article
- Multimodal dynamic and unclonable anti-counterfeiting using robust diamond microparticles on heterogeneous substrate.Nature communications · 2023Article
- A Reflectivity Enhanced 3D Optical Storage Nanostructure Application Based on Direct Laser Writing Lithography.Materials (Basel, Switzerland) · 2023Article
- Deep learning in light-matter interactions.Nanophotonics (Berlin, Germany) · 2022Review
- Computational spectrometers enabled by nanophotonics and deep learning.Nanophotonics (Berlin, Germany) · 2022Review
- Design of highly perceptible dual-resonance all-dielectric metasurface colorimetric sensor via deep neural networks.Scientific reports · 2022Article
- Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning.Nature communications · 2021Article
- Decoding Optical Data with Machine Learning.Laser & photonics reviews · 2021Article
- Deep learning beats the optical diffraction limit.Nature nanotechnology · 2019Article
- Emerging role of machine learning in light-matter interaction.Light, science & applications · 2019Article
- Deep-learning-powered photonic analog-to-digital conversion.Light, science & applications · 2019Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diffraction drastically limits the bit density in optical data storage. To increase the storage density, alternative strategies involving supplementary recording dimensions and robust readout schemes must be explored. Here, we propose to encode multiple bits of information in the geometry of subwavelength dielectric nanostructures. A crucial problem in high-density information storage concepts is the robustness of the information readout with respect to fabrication errors and experimental noise. Using a machine-learning-based approach in which the scattering spectra are analysed by an artificial neural network, we achieve quasi-error-free readout of sequences of up to 9 bits, encoded in top-down fabricated silicon nanostructures. We demonstrate that probing few wavelengths instead of the entire spectrum is sufficient for robust information retrieval and that the readout can be further simplified, exploiting the RGB values from microscopy images. Our work paves the way towards high-density optical information storage using planar silicon nanostructures, compatible with mass-production-ready complementary metal-oxide-semiconductor technology.
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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.