Machine learning holds promise for optimizing optical data storage technology

Data-intensive technologies, such as machine learning, place an increasing premium on new and improved data storage methods. The growing field of nanophotonics offers new storage techniques that are capable of holding petabytes of data on a single optical disc, providing necessary high capacity and reliable data storage.

Lamon et al. highlight the advances and challenges of integrating nanophotonics-enabled data storage and machine learning. Together, they promise new methods for high-resolution, fast and robust optical data writing and reading, in addition to aiding design of next-generation optical data storage.

“The application of new machine learning technologies in several scientific and industrial domains has resulted in remarkable and game-changing advances,” said author Simone Lamon. “In this context, machine learning technologies have demonstrated the potential to address long-standing challenges with nanophotonics-enabled optical data storage, such as improvements of storage capacity, energy consumption, lifetime, throughput, security and rewritability.”

The group proposes using deep learning algorithms for guiding adaptive optics and a technique that decreases laser beam distortions in optical systems. Such algorithms are well-positioned for reducing error rates in optical data reading and writing.

Additionally, image analysis enhanced by machine learning can optimize the ultra-high-resolution optical techniques and find patterns from the images needed to improve storage capacity and data throughput by orders of magnitude.

Inverse design methods are a high-throughput approach for developing configurations, architectures and materials for optical data storage through screening high volumes of candidates for a specific target performance. Machine learning can optimize such processes and aid to find novel designs more quickly.

Lamon hopes the group’s work stokes further interest in machine learning solutions for improving optical data storage enabled by nanophotonics.

Source: “Nanophotonics-enabled optical data storage in the age of machine learning,” by Simone Lamon, Qiming Zhang, and Min Gu, APL Photonics (2021). The article can be accessed at https://doi.org/10.1063/5.0065634 .

This paper is part of the Photonics and AI in Information Technologies Collection, learn more here .