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 |
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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. |
doi_str_mv | 10.1109/JSTQE.2024.3519983 |
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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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