Photonic time-delayed reservoir computing based on lithium niobate microring resonators

TL;DR

This paper introduces a lithium niobate micro-ring resonator based reservoir computing system that leverages nonlinear optical effects for high-performance, low-energy, on-chip photonic computation with multi-task capabilities.

Contribution

It presents the first RC scheme based on lithium niobate micro-ring resonators, exploiting unique nonlinear effects for enhanced computational performance.

Findings

Optimal wavelength detuning improves memory capacity and energy efficiency.

Three operational regions identified with distinct nonlinear behaviors.

WDM-based multi-task computing achieves performance comparable to single-task systems.

Abstract

On-chip micro-ring resonators (MRRs) have been proposed for constructing delay reservoir computing (RC) systems, offering a highly scalable, high-density computational architecture that is easy to manufacture. However, most proposed RC schemes have utilized passive integrated optical components based on silicon-on-insulator (SOI), and RC systems based on lithium niobate on insulator (LNOI) have not yet been reported. The nonlinear optical effects exhibited by lithium niobate microphotonic devices introduce new possibilities for RC design. In this work, we design an RC scheme based on a series-coupled MRR array, leveraging the unique interplay between thermo-optic nonlinearity and photorefractive effects in lithium niobate. We first demonstrate the existence of three regions defined by wavelength detuning between the primary LNOI micro-ring resonator and the coupled micro-ring array, where one region achieves an optimal balance between nonlinearity and high memory capacity at extremely low input energy, leading to superior computational performance. We then discuss in detail the impact of each ring's nonlinearity and the system's symbol duration on performance. Finally, we design a wavelength-division multiplexing (WDM) based multi-task parallel computing scheme, showing that the computational performance for multiple tasks matches that of single-task computations.