Pushing the limits of optical information storage using deep learning

TL;DR

This paper demonstrates a deep learning approach to enhance optical data storage density by encoding information in nanostructure geometry and robustly reading it out using neural networks, achieving high accuracy despite fabrication errors.

Contribution

It introduces a machine learning-based method for robustly decoding high-density optical data stored in silicon nanostructures, advancing towards practical, mass-producible storage solutions.

Findings

Quasi error-free readout of up to 9 bits in nanostructures

Robustness achieved with limited wavelength probing

Simplified readout using microscopy RGB values

Abstract

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.