Silicon Ring Resonator with Phase-Change Material as a Plastic Dynamical Node for Scalable All-Optical Neural Networks with Synaptic Plasticity

Synaptic plasticity, that is the ability of connections in neural networks to strengthen or weaken depending on their input, is a fundamental component of learning and memory in biological brains. We present a numerical and experimental investigation of an integrated photonic plastic node, consistin...

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Bibliographic Details
Published in2023 23rd International Conference on Transparent Optical Networks (ICTON) pp. 1 - 4
Main Authors Lugnan, Alessio, Carrillo, Santiago Garcia-Cuevas, Song, Junchao, Aggarwal, Samarth, Bruckerhoff-Pluckelmann, Frank, Pernice, Wolfram H. P., Bhaskaran, Harish, Wright, C. David, Bienstman, Peter
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.07.2023
Subjects
all-optical artificial neuron
Integrated photonics
neuromorphic computing
Optical fiber networks
Optical ring resonators
Plastics
Shape
Silicon
System-on-chip
Training
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Summary:Synaptic plasticity, that is the ability of connections in neural networks to strengthen or weaken depending on their input, is a fundamental component of learning and memory in biological brains. We present a numerical and experimental investigation of an integrated photonic plastic node, consisting of a silicon ring resonator enhanced by phase-change materials (GST). This all-optical device is capable of dynamical nonlinear behaviour, multi-scale volatile memory, non-volatile memory and multi-wavelength operations. We propose its employment as a building block in scalable all-optical dynamical neural networks that can adapt to their input via synaptic plasticity.
ISSN:2161-2064
DOI:10.1109/ICTON59386.2023.10207385