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

Yu Ming Ning, Qian Ma (), Qiang Xiao, Xin Xin Gao, Qian Wen Wu, Ze Gu, Rui Si Li, Long Chen, Jian Wei You and Tie Jun Cui ()
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Yu Ming Ning: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space
Qian Ma: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space
Qiang Xiao: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space
Xin Xin Gao: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space
Qian Wen Wu: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space
Ze Gu: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space
Rui Si Li: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space
Long Chen: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space
Jian Wei You: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space
Tie Jun Cui: Southeast University, State Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: 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 low thresholds and reconfigurable nonlinear activation functions. The system demonstrates remarkable classification capability in image classification and real-time human posture recognition tasks. Characterized by low latency, high speed, low power consumption, and flexible nonlinear activation, this architecture holds great promise for applications in security screening, medical rehabilitation, human-computer interaction, and numerous other fields.

Date: 2025
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DOI: 10.1038/s41467-025-65275-0

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