Scaling up for end-to-end on-chip photonic neural network inference Article Swipe
Bo Wu
,
Chaoran Huang
,
J. W. Zhang
,
Hailong Zhou
,
Yilun Wang
,
Jianji Dong
,
Xinliang Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41377-025-02029-z
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41377-025-02029-z
Optical neural networks are emerging as a competitive alternative to their electronic counterparts, offering distinct advantages in bandwidth and energy efficiency. Despite these benefits, scaling up on-chip optical neural networks for end-to-end inference is facing significant challenges. First, network depth is constrained by the weak cascadability of optical nonlinear activation functions. Second, the input size is constrained by the scale of the optical matrix. Herein, we propose a scaling up strategy called partially coherent deep optical neural networks (PDONNs). By leveraging an on-chip nonlinear activation function based on opto-electro-opto conversion, PDONN enables network depth expansion with positive net gain. Additionally, convolutional layers achieve rapid dimensionality reduction, thereby allowing for an increase in the accommodated input size. The use of a partially coherent optical source significantly reduces reliance on narrow-linewidth laser diodes and coherent detection. Owing to their broader spectral characteristics and simpler implementation, such sources are more accessible and compatible with scalable integration. Benefiting from these innovations, we designed and fabricated a monolithically integrated optical neural network with the largest input size and the deepest network depth, comprising an input layer with a size of 64, two convolutional layers, and two fully connected layers. We successfully demonstrate end-to-end two-class classification of fashion images and four-class classification of handwritten digits with accuracies of 96% and 94%, respectively, using an in-situ training method. Notably, performance is well maintained with partially coherent illumination. This proposed architecture represents a critical step toward realizing energy-efficient, scalable, and widely accessible optical computing.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41377-025-02029-z
- https://www.nature.com/articles/s41377-025-02029-z.pdf
- OA Status
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- References
- 44
- OpenAlex ID
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https://openalex.org/W4414291128Canonical identifier for this work in OpenAlex
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https://doi.org/10.1038/s41377-025-02029-zDigital Object Identifier
- Title
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Scaling up for end-to-end on-chip photonic neural network inferenceWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-09-17Full publication date if available
- Authors
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Bo Wu, Chaoran Huang, J. W. Zhang, Hailong Zhou, Yilun Wang, Jianji Dong, Xinliang ZhangList of authors in order
- Landing page
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https://doi.org/10.1038/s41377-025-02029-zPublisher landing page
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https://www.nature.com/articles/s41377-025-02029-z.pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41377-025-02029-z.pdfDirect OA link when available
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0Total citation count in OpenAlex
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