All-optical spiking processing and reservoir computing with a passive silicon microring and wavelength-time division multiplexing

Neuromorphic photonic systems offer significant advantages for parallel, high-speed, and low-power computing, among which spiking neural networks emerge as a powerful bio-inspired alternative. This study demonstrates, to our knowledge, a novel approach to all-optical spiking processing and reservoir computing using passive silicon microring resonators (MRRs). A key innovation is the demonstration of deterministic optical spiking and spectro-temporal coincidence detection without the need for pump-and-probe methods, simplifying the architecture and improving efficiency. By leveraging injection of excitatory optical signals at negative wavelength detuning relative to the MRR’s cold resonances, the system delivers prompt and high-contrast optical spiking events, essential for effective chip-integrated photonic spiking neural networks. Building on this, a photonic spiking reservoir computer is implemented using a single silicon MRR. The system encodes input information through a novel spectro-temporal scheme and classifies the Iris-Flower dataset with 92% accuracy. This performance is achieved with just 48 reservoir virtual nodes, averaging only three spikes per flower sample, hence highlighting the system’s efficiency and sparsity. These findings unlock novel neuromorphic photonic frameworks with MRRs, enabling sparse all-optical spiking processing and reservoir computing, particularly promising to be adapted in future coupled MRR structures and with binary output weights for light-enabled edge computing and sensing applications.