Photorefractive Integrated Photonics for Analog Signal Processing in AI

The computational cost of AI could be alleviated by accelerating the synaptic transfer calculations in artificial neural networks with an analog crossbar array processor. In this work, we present the core building blocks of an all-optical integrated photorefractive crossbar array for artificial neur...

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Veröffentlicht in:IEEE journal of selected topics in quantum electronics 2025-05, Vol.31 (3: AI/ML Integrated Opto-electronics), p.1-10
Hauptverfasser: Vlieg, Elger A., Dangel, Roger, Offrein, Bert J., Horst, Folkert
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container_issue 3: AI/ML Integrated Opto-electronics
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container_title IEEE journal of selected topics in quantum electronics
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creator Vlieg, Elger A.
Dangel, Roger
Offrein, Bert J.
Horst, Folkert
description The computational cost of AI could be alleviated by accelerating the synaptic transfer calculations in artificial neural networks with an analog crossbar array processor. In this work, we present the core building blocks of an all-optical integrated photorefractive crossbar array for artificial neural network training by demonstrating photorefractive synapses in an integrated 2-D beam interaction network. We show that the photorefractive quality of the circuits resembles that of the bulk GaAs crystal that they were fabricated from. Then, this work experimentally validates the integrated photorefractive crossbar array design and constitutes a framework for engineering photorefractive integrated photonics.
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subjects Analog crossbar multiply-accumulate (MAC) accelerator
Artificial neural networks
Bragg gratings
Diffraction
Gallium arsenide
holographic photonic memory
in-memory computing
integrated photonics
Laser beams
Optical device fabrication
Optical mixing
Photonics
photorefractive effect
Synapses
Vectors
title Photorefractive Integrated Photonics for Analog Signal Processing in AI
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