Error-Free Long-Lifespan Optical Storage Enhanced by Deep Learning

Optical information storage, in virtue of its large capacity, high stability, and long longevity, holds promising prospects in mass storage, while being limited by the trade-off between readout quality and error rate. The emerging intersection of optical storage and deep learning presents a valuable opportunity to achieve high-fidelity data storage. Here, a novel paradigm of error-free long-lifespan optical storage enhanced is proposed by deep learning, harnessing neural network to extract optical information from birefringence measurements. It is demonstrated that using neural networks outperforms traditional approaches in terms of efficiency and accuracy. Moreover, by adding extra birefringence information as input to the neural network, nearly 100%$100\%$ accuracy is achieved on an established five-bit dataset. Remarkably, even under extremely severe ambiguity, the paradigm still fulfills error-free readout and maintains a long lifespan. The experimental storage scheme is significantly conducive to the development of large-scale error-free storage, and paves the way for robust optical storage with environmental and temporal tolerance in practical scenarios. Optical information storage holds promising prospects in mass storage. A deep-learning-based optical storage scheme opens new avenues for high-fidelity optical information storage with a large capacity and long lifespan. This storage scheme is significantly conducive to the development of large-scale error-free storage, and paves the way for robust optical storage with environmental and temporal tolerance in practical scenarios. image