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Field-programmable photonic nonlinearity

Nature Photonics volume 19, pages 725–732 (2025)Cite this article

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Abstract

Propelled by advancements in artificial intelligence, the demand for field-programmable devices has grown rapidly in the last decade. Among various state-of-the-art platforms, programmable integrated photonics emerges as a promising candidate, offering a new strategy to drastically enhance computational power for data-intensive tasks. However, intrinsic weak nonlinear responses of dielectric materials have limited traditional photonic programmability to the linear domain, leaving out the most common and complex activation functions used in artificial intelligence. Here we push the capabilities of photonic field-programmability into the nonlinear realm by meticulous spatial control of distributed carrier excitations and their dynamics within an active semiconductor. Leveraging the architecture of photonic nonlinear computing through polynomial building blocks, our field-programmable photonic nonlinear microprocessor demonstrates in situ training of photonic polynomial networks with dynamically reconfigured nonlinear connections. Our results offer a new paradigm to revolutionize photonic reconfigurable computing, enabling the handling of intricate tasks using a polynomial network with unparalleled simplicity and efficiency.

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Fig. 1: Field-programmable photonic nonlinearity.
Fig. 2: Reconfigurable polynomial I/O connection.
Fig. 3: In situ training with field-programmable photonic nonlinearity for benchmark problem solving.
Fig. 4: Photonic polynomial networks for iris flower classification.
Fig. 5: Photonic polynomial networks for speech command recognition.

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Data availability

The main data that support this study are available within the Article and its Supplementary Information. The additional raw and processed data are available at https://doi.org/10.6084/m9.figshare.28560848.v1.

Code availability

The computer codes that support the plots within this paper and other findings of this study are available from the corresponding author on reasonable request.

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Acknowledgements

We acknowledge the support from Defense Advanced Research Projects Agency (DARPA) (grant no. W911NF-21-1-0340), Office of Naval Research (ONR) (grant no. N00014-23-1-2882) and National Science Foundation (NSF) (grant nos. ECCS-2023780, ECCS-2425529 and DMR-2326699). L.G. acknowledges the support from the National Science Foundation (NSF) (grant nos. PHYS-1847240 and DMR-2326698).

Author information

Authors and Affiliations

  1. Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA, USA

    Tianwei Wu & Liang Feng

  2. Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA

    Yankun Li & Liang Feng

  3. Department of Physics and Astronomy, College of Staten Island, CUNY, Staten Island, NY, USA

    Li Ge

  4. The Graduate Center, CUNY, New York, NY, USA

    Li Ge

Authors
  1. Tianwei Wu
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  3. Li Ge
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Contributions

T.W. and L.F. conceived the project. T.W. developed the in situ training algorithm for nonlinear networks, fabricated the devices, and performed optical measurements and digital network simulations. Y.L., T.W. and L.G. developed theoretical models for nonlinear photonic transmission. L.F. guided the research. All authors participated in discussions and contributed to the writing of the paper.

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Correspondence to Liang Feng.

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The authors declare no competing interests.

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Nature Photonics thanks Fei Xia and Jianping Yao for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Optical measurement.

a, Set-up for the measurement. VA: variable attenuator; OBJ: objective; DM: dichroic mirror; BPF: band-pass filter. b, An image captured by the camera, with the influence of the PL removed by subtracting a reference image taken without input signals. The white dashed boxes on the left indicate the integration area for input port powers, while the yellow dashed boxes on the right highlight the integration area for output port powers. c, Calibration of the digital readout of the camera, performed using a large external laser light spot that covers most of the camera’s working area. The results show a convincing linear input–output power response within the working range.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12 and Tables 1–5.

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Wu, T., Li, Y., Ge, L. et al. Field-programmable photonic nonlinearity. Nat. Photon. 19, 725–732 (2025). https://doi.org/10.1038/s41566-025-01660-x

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