Article | Published:

Pushing the limits of optical information storage using deep learning

Nature Nanotechnologyvolume 14pages237244 (2019) | Download Citation

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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Code availability

The authors declare that all software used to obtain the results of this work is publicly accessible as open-source software: python including SciPy, TensorFlow, as well as pyGDM46, our own implementation of the GDM. Our scripts can be made accessible from the corresponding author upon reasonable request.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. The experimental and simulated scattering data sets are available under https://doi.org/10.6084/m9.figshare.7326842.v1.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

The authors thank A. Arbouet and C. Girard for their advice, for their help and for discussing and proofreading the manuscript, and F. Carcenac for his help with EBL and automatic SEM images. This work was supported by Programme Investissements d’Avenir under the program ANR-11-IDEX-0002-02, reference ANR-10-LABX-0037-NEXT, by the LAAS-CNRS micro and nanotechnologies platform, a member of the French RENATECH network, and by the computing facility centre CALMIP of the University of Toulouse under grant P12167.

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Affiliations

  1. CEMES, Université de Toulouse, CNRS, Toulouse, France

    • Peter R. Wiecha
  2. LAAS, Université de Toulouse, CNRS, INP, Toulouse, France

    • Aurélie Lecestre
    • , Nicolas Mallet
    •  & Guilhem Larrieu

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Contributions

P.R.W. conceived the idea and designed the research together with G.L. G.L. and A.L. developed the fabrication techniques. A.L. fabricated the nanostructures and performed the electron microscopy with the help of N.M. P.R.W. carried out the optical experiments, did the simulations and the data analysis, and implemented the machine-learning part. P.R.W. wrote the manuscript with contributions from G.L. P.R.W and G.L. discussed the results and all authors commented on the manuscript at every stage.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Peter R. Wiecha.

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https://doi.org/10.1038/s41565-018-0346-1

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