High-fidelity robust decoding of multiplexed recording by deep learning

Multiplexing information in light's fundamental attributes to create supplementary orthogonal data channels has been well heralded as an effective means for optical data storage with greatly enhanced capacities. However, robust decoding methods against inevitable crosstalks associated with experimental noise and writing imperfections as the increase of multiplexing dimensions represent a major hurdle preventing the effective practice of multi-dimensional optical recording. Here, we propose a deep-learning-based retrieval approach for robust decoding multiplexed information. An artificial neural network is trained to learn the crosstalks from multiplexed recording in disordered gold nanorod aggregates with loosened orthogonality constraints. The acquired raw readout images are analyzed by the trained neural network, which allows quick, high-fidelity, and reliable information retrieval from polarization-, wavelength-, and 3D spatially multiplexed data. The smart decoding protocol paves the way toward the mass-production ready and wide-spread application of high-capacity multi-dimensional optical storage.