Neuromorphic Photonic Circuits with Nonlinear Dynamics and Memory for Time Sequence Classification

Photonic neuromorphic computing offers compelling advantages in power efficiency and parallel processing, but often falls short in realizing scalable nonlinearity and long-term memory. We overcome these limitations by employing silicon microring resonator (MRR) networks. These integrated photonic circuits enable compact, high-throughput neuromorphic computing by simultaneously exploiting spatial, temporal, and wavelength dimensions. This work advances the investigation of MRR networks for photonics-based machine learning (ML). We demonstrate the system's effectiveness on two widely used image classification benchmarks, MNIST and Fashion-MNIST, by encoding images directly into time sequences. In particular, we enhance the computational performance of a linear readout classifier within the reservoir computing paradigm through the strategic use of multiple physical output ports, diverse laser wavelengths, and varied input power levels. Moreover, we achieve substantially improved accuracies in a single-pixel classification setting without relying on digital memory, thanks to the inherent memory and parallelism of our MRR network.