All optical on-chip nonlinear activation functions for multi-wavelength computation

This work proposes an all-optical on-chip nonlinear activation function unit for multi-wavelength computing applications. Our proposed unit utilizes the unique properties of silicon, specifically the two-photon absorption and thermo-optic effects, to achieve nonlinearity essential for optical neural networks. We designed the parameters of the corresponding unit structure through simulation, fabricated and tested the actual structure experimentally, and obtained a CELU-like response curve. In practical classification tasks, we evaluated its performance in various classification tasks through numerical simulation and achieved a recognition accuracy of 98.84<i>%</i> on the MNIST dataset. This work addresses a critical gap in multi-wavelength systems and contributes to the development of more powerful and efficient optical computing platforms.

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Publication Details

Journal
Optics Express
Published
2025-04-29
DOI
https://doi.org/10.1364/oe.558267
Citations
2
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
4.96

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All optical on-chip nonlinear activation functions for multi-wavelength computation

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2 citations
Optics Express
Neural Networks and Reservoir Computing
4.96
article

All optical on-chip nonlinear activation functions for multi-wavelength computation

Qilin Hong, Ping Xu, Zhihong Zhu, Pingyu Zhu, Gongyu Xia, Jiacheng Liu
article en
2 citations

Abstract

This work proposes an all-optical on-chip nonlinear activation function unit for multi-wavelength computing applications. Our proposed unit utilizes the unique properties of silicon, specifically the two-photon absorption and thermo-optic effects, to achieve nonlinearity essential for optical neural networks. We designed the parameters of the corresponding unit structure through simulation, fabricated and tested the actual structure experimentally, and obtained a CELU-like response curve. In practical classification tasks, we evaluated its performance in various classification tasks through numerical simulation and achieved a recognition accuracy of 98.84<i>%</i> on the MNIST dataset. This work addresses a critical gap in multi-wavelength systems and contributes to the development of more powerful and efficient optical computing platforms.

Optics ExpressVol. 33(10)
National University of Defense Technology (CN)
National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 6%
Neural Networks and Reservoir Computing
4.96
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