Digital and plasmonic artificial neural networks—Improved nonlinear signal processing at high speed and low complexity

Transmission at ever higher data rates increasingly demands more advanced digital signal processing techniques, raising both power consumption and operational costs. Here, we introduce a photonic/plasmonic artificial neural network (ANN) using plasmonic modulators to directly mitigate nonlinear signal distortions carried by an optical carrier. This first-of-its-kind plasmonic ANN achieves an ultracompact footprint and high-speed operation and markedly reduces the need for electronic processing. We compare our plasmonic ANN against a traditional digital feed-forward equalizer and a Volterra series, as well as the corresponding digital ANN. The results demonstrate that an astonishingly small ANN outperforms classical equalizers by attaining higher SNR at smaller computational effort. While the digital ANN offers an ideal implementation, executing the ANN on our first plasmonic chip already shows remarkable equalization performance with minimal components. The findings reveal a path toward ultracompact, high-speed, power-efficient, low-latency alternatives to conventional signal processing.