Spoof Plasmonic Neural Networks for Crosstalk Decoupling in Millimeter‐Wave Communication Systems

ABSTRACT Crosstalk has emerged as a critical bottleneck in high‐density integration and high‐speed data transmission scenarios. Conventional mitigation strategies may often be limited by increased system size, elevated energy consumption, and restricted decoupling speed. Here, we propose a crosstalk decoupling scheme based on a spoof plasmonic neural network (SPNN), which leverages the unique electromagnetic characteristics of spoof plasmonic structures alongside the parallel computing capabilities of diffractive neural networks for efficient and high‐speed signal processing. The experimental results demonstrate that SPNN substantially reduces inter‐channel crosstalk while enhancing both data transmission efficiency and signal integrity. Specifically, a channel employing the SPNN achieves a data rate of 12.5 Gbps with 32‐quadrature amplitude modulation, even amidst adjacent channel interference. Furthermore, the SPNN facilitates real‐time video transmission under a co‐frequency channel interference, a feat unattainable in the absence of the SPNN. Our findings establish the SPNN‐based crosstalk decoupling as a promising and practical solution for next‐generation high‐density integrated communication systems, with broad implications for advancing information technologies.