Deep diffractive optical neural networks for detecting Skyrmionic topologies of light

TL;DR

This paper introduces a deep diffractive optical neural network that accurately detects Skyrmionic topologies of light, enabling robust topological encoding and decoding for optical information processing.

Contribution

It presents the first deterministic detector for optical Skyrmions using a deep diffractive neural network with reduced training complexity and high noise robustness.

Findings

High accuracy detection of 81 topologies

Robust performance with significant noise levels

Successful transmission of topologically encoded images

Abstract

Optical Skyrmions are topological forms of structured light with the potential of an infinite encoding alphabet that is immune to disturbance. This attractive prospect is hindered by the lack of any topological detector, a challenging problem due to the non-orthogonal nature of the topological invariant (N). Here we demonstrate the first deterministic detector for Skyrmionic topologies of light using a deep diffractive optical neural network. Our network uses two independent processing channels of 5 diffractive layers each to map incoming topologies to spatially separated Gaussian channels from which N can be detected. We overcome the complexity of the training by using a spatial mode basis rather than pixels, reducing the training variables by x1000 compared to current methods. We use the detector on an input set of 81 input topologies, showing high accuracy even in the presence of significant levels of noise. Finally, to show the practical utility of the device, we transmit and receive an image encoded in a 14-level topological alphabet with no discernible cross-talk. Our work offers a new paradigm for the emergent field of diffractive optical networks and can easily be extended to other forms of optical topologies, setting a clear pathway for their deployment in real-world applications.