Micro-Ring Perceptron Sensor for High-Speed, Low-Power Radio-Frequency Signal

Radio-frequency (RF) sensing enables long-range, high-resolution detection for applications such as radar and wireless communication. RF photonic sensing mitigates the bandwidth limitations and high transmission losses of electronic systems by transducing the detected RF signals into broadband optical carriers. However, these sensing systems remain limited by detector noise and Nyquist rate sampling with analog-to-digital converters, particularly under low-power and high-data rate conditions. To overcome these limitations, we introduce the micro-ring perceptron (MiRP) sensor, a physics-inspired AI framework that integrates the micro-ring (MiR) dynamics-based analog processor with a machine-learning-driven digital backend. By embedding the nonlinear optical dynamics of MiRs into an end-to-end architecture, MiRP sensing maps the input signal into a learned feature space for the subsequent digital neural network. The trick is to encode the entire temporal structure of the incoming signal into each output sample in order to enable effectively sub-Nyquist sampling without loss of task-relevant information. Evaluations of three target classification datasets demonstrate the performance advantages of MiRP sensing. For example, on MNIST, MiRP detection achieves $94\pm0.1$\% accuracy at $1/49$ the Nyquist rate at the input RF signal of $1$~ pW, compared to $11\pm0.4$\% for the conventional RF detection method. Thus, our sensor framework provides a robust and efficient solution for the detection of low-power and high-speed signals in real-world sensing applications.