Nonreciprocal surface plasmonic neural network for decoupled bidirectional analogue computing

To address the burgeoning demand for computing capacity in artificial intelligence, researchers have explored optical neural networks that show advantages of ultrafast speed, low power consumption, ultra-high bandwidth, and high parallelism. However, most existing optical networks are reciprocal, where forward and backward propagation are intrinsically coupled. This results in the backward pathway remaining largely unexplored, hindering the realization of integrated perception-response systems. Here, we present a nonreciprocal neural network leveraging enhanced magneto-optical effect in spoof surface plasmon polaritons transmission line to decouple forward and backward paths. Moreover, the computing function of the network can be flexibly modulated by the magnetization orientation in ferrites and variations in operating frequency. We demonstrate broadband bidirectional decoupled image processing across various operators, where the operator configuration can be precisely designed by encoding the input signals. This decoupling achieves independent control and signal isolation within the same structure, effectively emulating the unidirectional transmission of biological networks. Furthermore, matrix-solving operations can be facilitated by incorporating feedback waveguides for desired recursion paths. Our findings open pathways to nonreciprocal architectures for independent bidirectional algorithms in analogue computing. Unidirectional propagation is a characteristic of biological networks, but remains challenging in optical network. Here, authors present a nonreciprocal surface plasmonic network that shows asymmetric scattering matrices, promising decoupled computing in forward and backward paths.