Compact All optical Reservoir Computing via Luminescence Dynamics in Rare-earth Ions-doped Nanocrystals

TL;DR

This paper introduces a compact all-optical reservoir computing system using rare-earth ion-doped nanocrystals, leveraging their nonlinear luminescence and multitimescale memory for high-speed, energy-efficient neuromorphic computing.

Contribution

It demonstrates the first all-optical reservoir computing platform based on rare-earth nanocrystals, eliminating bulky components and enhancing scalability and simplicity.

Findings

Achieved 90.7% accuracy in MNIST digit classification.

Demonstrated low-error chaotic time-series prediction with NRMSE < 0.1.

Reduced system footprint and complexity compared to traditional approaches.

Abstract

Optical neuromorphic computing offers a promising route to high speed, energy efficient information processing. However, photonic neurons, as the critical components for enhancing computational expressivity, still face significant bottlenecks in nonlinear mapping and memory capacity. Here, we demonstrate an all optical reservoir computing system based on rare earth ions doped nanocrystals for the first time, leveraging their intrinsic nonlinear luminescence dynamics and multitimescale memory. Unlike traditional schemes that require bulky optical delays or intricate resonant structures, our platform exploits the material's inherent properties: nonlinear cross-relaxation processes enable nonlinear mapping while millisecond-scale metastable energy levels provide fading memory. As a proof of concept, we achieve 90.7% accuracy in MNIST digit classification and low-error chaotic time-series prediction (NRMSE < 0.1) using the rare-earth ions based system. Our work significantly reduce system footprint and complexity, offering a scalable, fully optical solution for edge computing and real-time neuromorphic applications.