Compact, Large-Scale Photonic Neurons by Modulation-and-Weight Microring Resonators

TL;DR

This paper introduces a scalable, compact photonic neuron architecture using microring resonators that perform modulation and weighting simultaneously, enabling high-speed, energy-efficient neuromorphic photonic computing with adaptable dynamics.

Contribution

The authors present a novel microring resonator design that combines modulation and weighting, reducing footprint and spectral alignment requirements, and supporting reconfigurable, recurrent neural functionalities.

Findings

Demonstrated spatial and temporal computing tasks including image processing and time-series prediction.

Achieved high compute density of 4.67 TOPS/s/mm^2 and tuning efficiency of 105 TOPs/W.

Supported multiple operational modes with low power consumption and high flexibility.

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 \SI{}{\micro\meter^2} and consumes an average of \SI{0.186}{\milli\watt}, corresponding to a compute density of \SI{4.67}{TOPS/s/\milli\meter^2}. Excluding electronic power, the on-chip tuning efficiency reaches approximately \SI{105}{TOPs/\watt}, 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.