Authors:	A. Foradori, A. Lugnan, L. Pavesi, P. Bienstman
Title:	Experimental investigation of time series classification using a self-pulsing microring resonator network
Format:	International Journal
Publication date:	6/2026
Journal/Conference/Book:	Applied Physics Letters - Photonics
Editor/Publisher:	AIP Publishing,
Volume(Issue):	11(6)
DOI:	10.1063/5.0329322
Citations:	Look up on Google Scholar
Download:	(9.5MB)

Abstract

Photonic neuromorphic computing offers compelling advantages in power efficiency and parallel processing, but it often falls short in realizing scalable nonlinearity and long-term memory. These limitations can be overcome by silicon microring resonator (MRR) networks. These integrated photonic circuits enable compact, high-throughput neuromorphic computing by simultaneously exploiting spatial, temporal, and wavelength dimensions. This work provides an in-depth study of MRR networks for photonics-based machine learning. We investigate the system’s effectiveness on two widely used image classification benchmarks, MNIST and Fashion-MNIST, by encoding images directly into time sequences. In particular, we enhance the computational performance of a linear readout classifier within the reservoir computing paradigm through the strategic use of multiple physical output ports, diverse laser wavelengths, and varied input power levels. Moreover, we explore a single-pixel classification setting, where inference does not require digital memory, thanks to the inherent memory and parallelism of our MRR network.

Related Research Topics

• Photonic Reservoir Computing

Related Projects

• Horizon Europe: Neuropuls (NEUROmorphic energy‑efficient secure accelerators based on Phase change materials aUgmented siLicon photonicS)