All-optical nonlinear activation functions realized on phase-change photonic integrated circuits with microheaters

Abstract Photonic neural networks have garnered significant attention in recent years due to their ultra-high computational speed, broad bandwidth, and parallel processing capabilities. However, compared to conventional electronic nonlinear activation function (NAF), progress on efficient and easily implementable optical nonlinear activation function (ONAF) was barely reported. To address this issue, we proposed a programmable, low-loss ONAF device based on a silicon micro-ring resonator capped with the Antimony selenide (Sb 2 Se 3 ) thin films, and with indium tin oxide (ITO) used as the microheater. Leveraging our self-developed phase-transformation kinetic and optical models, we successfully simulated the phase-transition behavior of Sb 2 Se 3 and three different ONAFs—ELU, ReLU, and radial basis function (RBF) were achieved according to discernible optical responses of proposed devices under different phase-change extents. Classification results from the Fashion MNIST dataset demonstrated that these ONAFs can be considered as appropriate substitutes for traditional NAF. This indicated the bright prospect of the proposed device for nonlinear activation function in future photonic neural networks.

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Publication Details

Journal
Journal of Semiconductors
Published
2025-02-01
DOI
https://doi.org/10.1088/1674-4926/24090045
Citations
8
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
19.85
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article

All-optical nonlinear activation functions realized on phase-change photonic integrated circuits with microheaters

Weijia Zhou, Wen Huang, Bingxin Ding, Jiyuan Jiang et al.
8 citations
Journal of Semiconductors
Neural Networks and Reservoir Computing
19.85
article

All-optical nonlinear activation functions realized on phase-change photonic integrated circuits with microheaters

Weijia Zhou, Wen Huang, Bingxin Ding, Jiyuan Jiang, Xiaojuan Lian, Jieyun Wu, Xiao Yan Liu, Xin Zhang, Lei Wang, Hai-Hua Wang, Shiyu Li
article en
8 citations

Abstract

Abstract Photonic neural networks have garnered significant attention in recent years due to their ultra-high computational speed, broad bandwidth, and parallel processing capabilities. However, compared to conventional electronic nonlinear activation function (NAF), progress on efficient and easily implementable optical nonlinear activation function (ONAF) was barely reported. To address this issue, we proposed a programmable, low-loss ONAF device based on a silicon micro-ring resonator capped with the Antimony selenide (Sb 2 Se 3 ) thin films, and with indium tin oxide (ITO) used as the microheater. Leveraging our self-developed phase-transformation kinetic and optical models, we successfully simulated the phase-transition behavior of Sb 2 Se 3 and three different ONAFs—ELU, ReLU, and radial basis function (RBF) were achieved according to discernible optical responses of proposed devices under different phase-change extents. Classification results from the Fashion MNIST dataset demonstrated that these ONAFs can be considered as appropriate substitutes for traditional NAF. This indicated the bright prospect of the proposed device for nonlinear activation function in future photonic neural networks.

Journal of SemiconductorsVol. 46(2)
Nantong University (CN), Nanjing University of Posts and Telecommunications (CN), Zhejiang University (CN)
Openalex Percentile: Top 1%
Neural Networks and Reservoir Computing
19.85
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