Nonreciprocal surface plasmonic neural network for decoupled bidirectional analogue computing.

ABSTRACT: 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 ferri

SUBMITTER: Li X 

PROVIDER: S-EPMC12365158 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature