Multilayer nonlinear diffraction neural networks with programmable and fast ReLU activation function.

ABSTRACT: Optical diffractive neural networks are emerging for improving speed and energy efficiency in machine learning. However, the challenges of nonlinear activation functions (e.g., latency issues, high power consumption, and cascading complexity) impede their performance and practical deployment. Here, we propose a programmable multilayer full-space nonlinear neural network operating in the microwave frequency band. Its nonlinear layers are constructed using programmable metasurfaces integrated with RF components, implementing a ReLU-like activation function. The nonlinear architecture achieves a nanosecond-scale delay (17.7 ns), representing orders of magnitude improvement in speed over photoelectric conversion-based nonlinearities. Moreover, the nonlinearity is characterized by exceedingly l

SUBMITTER: Ning YM 

PROVIDER: S-EPMC12644553 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature