Compact, reconfigurable, and scalable photonic neurons by modulation-and-weighting microring resonators

Abstract Neuromorphic photonics promises sub-nanosecond latency, ultrawide bandwidth, and high parallelism, but practical scalability is constrained by fabrication tolerances, spectral alignment, and tuning energy. Here, we present a large-scale, compact, and reconfigurable photonic neuron in which each microring performs modulation and weighting simultaneously. By exploiting both carrier and thermal tuning within a single device, this architecture reduces footprint, relaxes spectral alignment requirements to just two optical components, and yields a steep transfer response that lowers tuning energy. The proposed neuron supports multiple operating configurations, allowing its dynamical behavior to be adapted to different computational tasks. In particular, a short electrical feedback path enables recurrent operation, providing tunable short- and long-term memory for temporal processing. Using a 10-microring resonator array, we demonstrate both spatial and temporal computing, including a 3 $$\times$$ × 3 convolution for image processing with an error of <5% and high-frequency financial time-series prediction. Each modulation-weighting element occupies 80 $$\times$$ × 45 $$\upmu$$ μ m $$^2$$ 2 and consumes an average of 0.186 mW, corresponding to a compute density of 4.67 TOPS/s/mm $$^2$$ 2 . Excluding electronic power, the on-chip tuning efficiency reaches approximately 105 TOPs/W, which is comparable to state-of-the-art implementations. These results indicate that modulation-and-weighting microring resonator banks provide a scalable building block for large-scale neuromorphic photonic systems, offering a favorable combination of compact footprint, low power consumption, and functional flexibility.