Self-PulsingMicroring Resonator Networks for Bandwidth-EfficientEvent Detection in an Optical Fiber Sensor

Abstract The native processing of time-dependent signals from optical sensors by integrated photonic circuits can potentially bring significant advantages in terms of energy consumption, latency and processing power, as it allows skipping or reducing the use of fast digital electronics and directly exploiting optical degrees of freedom and parallelism. However, due to a short memory, optical operations usually struggle to directly process optical signals with relatively slow (<MHz) dynamics from optical sensors. In this work, we experimentally show that these limitations can be overcome by exploiting the self-pulsing dynamics in a microring resonator (MRR) network. In particular, we demonstrate that such dynamics can expand and retain information about perturbations sensed by a fiber sensor. This reduces the minimum sampling rate for the digitization of the sensor signal by at least 1 order of magnitude. A noise sensitivity analysis further shows that this advantage is robust against the noise of the photodetector. The reduction is achieved by combining fiber sensing measurements at two different perturbation locations and frequencies with MRR network measurements at multiple output ports, input power levels and laser wavelengths. This work represents a first step in bridging time-dependent optical processing and optical sensing at sub-μs time scales.

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Publication Details

Journal
ACS Photonics
Published
2026-08-18
DOI
https://doi.org/10.1021/acsphotonics.6c01478
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

Self-PulsingMicroring Resonator Networks for Bandwidth-EfficientEvent Detection in an Optical Fiber Sensor

Stefano Biasi, Ilya Auslender, Yonas Muanenda, Alessio Lugnan et al.
ACS Photonics
Neural Networks and Reservoir Computing
article

Self-PulsingMicroring Resonator Networks for Bandwidth-EfficientEvent Detection in an Optical Fiber Sensor

Stefano Biasi, Ilya Auslender, Yonas Muanenda, Alessio Lugnan, F. Di Pasquale, Claudio J. Oton, Lorenzo Pavesi
article en

Abstract

Abstract The native processing of time-dependent signals from optical sensors by integrated photonic circuits can potentially bring significant advantages in terms of energy consumption, latency and processing power, as it allows skipping or reducing the use of fast digital electronics and directly exploiting optical degrees of freedom and parallelism. However, due to a short memory, optical operations usually struggle to directly process optical signals with relatively slow (<MHz) dynamics from optical sensors. In this work, we experimentally show that these limitations can be overcome by exploiting the self-pulsing dynamics in a microring resonator (MRR) network. In particular, we demonstrate that such dynamics can expand and retain information about perturbations sensed by a fiber sensor. This reduces the minimum sampling rate for the digitization of the sensor signal by at least 1 order of magnitude. A noise sensitivity analysis further shows that this advantage is robust against the noise of the photodetector. The reduction is achieved by combining fiber sensing measurements at two different perturbation locations and frequencies with MRR network measurements at multiple output ports, input power levels and laser wavelengths. This work represents a first step in bridging time-dependent optical processing and optical sensing at sub-μs time scales.

ACS Photonics
Scuola Superiore Sant'Anna (IT), University of Trento (IT)
Affordable and clean energy
Openalex Percentile: Top 39%
Neural Networks and Reservoir Computing
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