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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Published in | 2023 23rd International Conference on Transparent Optical Networks (ICTON) pp. 1 - 4 |
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Main Authors | , , , , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
02.07.2023
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Subjects | |
Online Access | Get full text |
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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. |
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ISSN: | 2161-2064 |
DOI: | 10.1109/ICTON59386.2023.10207385 |