Investigation of recurrent photonic neural network using optical-electrical-optical converters with RC delay

Photonic neural networks (PNNs) have been attracting intense attention owing to their low latency and low-power consumption. Among the PNNs, the optoelectronic recurrent neural network (RNN) enables low-power and high-speed time-series data processing using a compact loop structure. However, the RNN performance can be limited by the resistive-capacitive (RC) delay of optical-electrical-optical (OEO) converters. Here, we study in simulation an optoelectronic RNN equipped with OEO converters with RC delay. We confirm the correct RNN behavior even when RC delay is comparably large to the time interval of data. Our simulation results show that the optoelectronic RNN achieves 80% test accuracy for the sequential MNIST task and that the accuracy increases with RC delay when loop losses exist. Our analyses reveal that the accumulation of time-series data by RC delay does not degrade the RNN performance but rather can compensate for the degraded RNN performance due to loop losses.