  Diffraction drastically limits the bit density in optical data storage. To
increase the storage density, alternative strategies involving supplementary
recording dimensions and robust read-out 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 analyzed by an artificial neural
network, we achieve quasi error free read-out of sequences of up to 9 bit,
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 CMOS technology.
