Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning
This addresses the challenge of efficient data transport in optical storage systems, offering a method to overcome computational scaling issues, though it appears incremental as it builds on existing machine-learning and simulation techniques.
The paper tackled the problem of decoding large-scale digital images transmitted through multi-mode fibers for optical storage, achieving retrieval of up to 66 kB using a machine-learning approach with a U-Net and differentiable ray tracing.
Retrieving images transmitted through multi-mode fibers is of growing interest, thanks to their ability to confine and transport light efficiently in a compact system. Here, we demonstrate machine-learning-based decoding of large-scale digital images (pages), maximizing page capacity for optical storage applications. Using a millimeter-sized square cross-section waveguide, we image an 8-bit spatial light modulator, presenting data as a matrix of symbols. Normally, decoders will incur a prohibitive O(n^2) computational scaling to decode n symbols in spatially scrambled data. However, by combining a digital twin of the setup with a U-Net, we can retrieve up to 66 kB using efficient convolutional operations only. We compare trainable ray-tracing-based with eigenmode-based twins and show the former to be superior thanks to its ability to overcome the simulation-to-experiment gap by adjusting to optical imperfections. We train the pipeline end-to-end using a differentiable mutual-information estimator based on the von-Mises distribution, generally applicable to phase-coding channels.