Laser writing in glass for dense, fast and efficient archival data storage | Nature
www.nature.com
Long-term preservation of digital information is vital for safeguarding the knowledge of humanity for future generations. Existing archival storage solutions, such as magnetic tapes and hard disk drives, suffer from limited media lifespans that render them unsuitable for long-term data retention1–3. Optical storage approaches, particularly laser writing in robust media such as glass, have emerged as promising alternatives with the potential for increased longevity. Previous work4–16 has predominantly optimized individual aspects such as data density but has not demonstrated an end-to-end system, including writing, storing and retrieving information. Here we report an optical archival storage technology based on femtosecond laser direct writing in glass that addresses the practical demands of archival storage, which we call Silica. We achieve a data density of 1.59 Gbit mm−3 in 301 layers for a capacity of 4.8 TB in a 120 mm square, 2 mm thick piece of glass. The demonstrated write regimes enable a write throughput of 25.6 Mbit s−1 per beam, limited by the laser repetition rate, with an energy efficiency of 10.1 nJ per bit. Moreover, we extend the storage ability to borosilicate glass, offering a lower-cost medium and reduced writing and reading complexity. Accelerated ageing tests on written voxels in borosilicate suggest data lifetimes exceeding 10,000 years. An optical archival storage technology based on femtosecond laser direct writing in glass addresses the practical demands of archival storage.
    • FauxLiving@lemmy.worldEnglish
      5·
      2 days ago

      It’s a way to infer the data without having to create some human engineered and fragile detection method.

      The problem of dealing with unreliable signal transmission (i.e. a CNN’s error rate at inferring the data based on their imaging) is well explored. A CNN that fails to correctly read some measurable percentage of time is not much different than a wireless data transmission on a noisy channel.

      You solve the problem by encoding the signal so that you can check the data as it comes in to discover and correct for errors. A simple example would be writing the data 3 times so that you could compare the inference on each of the 3 places where the data is written. Modern error checking algorithms can do a lot better than this, space-wise.

      CNNs can be trained to have a very high accuracy rate on these kinds of image recognition tasks (especially with a limited symbol set) and Microsoft can tune their error correction around the CNN’s error rates so the net result would be a clean and error check and corrected output.

      Not to mention that CNNs may not be required of future persons with better imaging technology.