Reconfigurable versatile integrated photonic computing chip

Yufei Wang; Kun Liao; Kuo Zhang; Zhuochen Du; Ze Wang; Bo Ni; Tianyu Xu; Shuai Feng; Yan Yang; Qi-Fan Yang; Quan Sun; Xiaoyong Hu; Qihuang Gong

doi:10.1186/s43593-025-00098-6

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Reconfigurable versatile integrated photonic computing chip

eLight Vol. 5, (2025)

Research Article | Updated：2025-12-17

- Reconfigurable versatile integrated photonic computing chip
- Affiliations：
- Author bio：
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- DOI：10.1186/s43593-025-00098-6
  CLC：
- Received：08 April 2025，
  
  Revised：2025-07-07，
  
  Accepted：10 July 2025，
  
  Published Online：18 August 2025，
  
  Published：2025-12
- Accepted：
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Yufei Wang, Kun Liao, Kuo Zhang, et al. Reconfigurable versatile integrated photonic computing chip[J]. eLight, 2025, 5.
DOI：

Yufei Wang, Kun Liao, Kuo Zhang, et al. Reconfigurable versatile integrated photonic computing chip[J]. eLight, 2025, 5. DOI： 10.1186/s43593-025-00098-6.

摘要

Abstract

With the rapid development of information technology

artificial intelligence and large-scale models have exhibited exceptional performance and widespread applications. Photonic hardware offers a promising solution to meet the growing demands for

computational power and energy efficiency. Researchers have aimed to develop an efficient integrated photonic computing chip capable of supporting a wide range of application scenarios in both static and dynamic temporal domains. However

with several mainstream photonic components already well-developed

achieving fundamental breakthroughs at the level of basic computing units remains highly challenging. Here

we report a novel algorithm-hardware co-design strategy that enables in situ reconfigurability across diverse neural network models

all within a unified photonic configuration. We unlock the intrinsic capabilities of a compact cross-waveguide coupled microring component to natively support both static and dynamic temporal tasks. As a proof of concept

we experimentally integrated a turnkey soliton microcomb as the light source on the photonic computing platform

demonstrating the realization of fully connected

convolutional

and recurrent neural network models within a unified structure. The chip achieves area computing efficiency of up to 2.45 TOPS/mm

for 208 tunable components. We evaluate the performance of the proposed chip by implementing image classification tasks on the MNIST and CIFAR-10 datasets

achieving measured test accuracies of 92.93% and 56.57%

respectively. Sentiment analysis on the IMDB dataset achieves a measured test accuracy of 80.81%. Furthermore

speech recognition is implemented by combining three neural networks within a scaled-up architecture. This work addresses the challenges of performing versatile computations on integrated photonic platforms

offering a promising solution for chip-integrated multifunctional photonic information processing.

关键词

Keywords

references

T.B. Brown et al. , Language models are few-shot learners . Adv. Neural. Inf. Process. Syst. 33 , 1877 - 1901 ( 2020 ).

E. Strubell , A. Ganesh , A. McCallum , Energy and policy considerations for modern deep learning research . AAAI 34 , 13693 ( 2020 ). http://doi.org/10.1609/aaai.v34i09.7123 http://doi.org/10.1609/aaai.v34i09.7123

G. Huang, Z. Liu, L. Van Der Maaten, K. Q. Weinberger, Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Honolulu, HI, 2017), pp. 2261–2269.

R. Li , Y. Gong , H. Huang , Y. Zhou , S. Mao , Z. Wei , Z. Zhang , Photonics for neuromorphic computing: fundamentals, devices, and opportunities . Adv. Mater. 37 , 2312825 ( 2025 ). http://doi.org/10.1002/adma.202312825 http://doi.org/10.1002/adma.202312825

H. Zhou et al. , Photonic matrix multiplication lights up photonic accelerator and beyond . Light Sci. Appl. 11 , 30 ( 2022 ). http://doi.org/10.1038/s41377-022-00717-8 http://doi.org/10.1038/s41377-022-00717-8

P.L. McMahon , The physics of optical computing . Nat. Rev. Phys. 5 , 717 ( 2023 ). http://doi.org/10.1038/s42254-023-00645-5 http://doi.org/10.1038/s42254-023-00645-5

S. Hua et al. , An integrated large-scale photonic accelerator with ultralow latency . Nature 640 , 361 ( 2025 ). http://doi.org/10.1038/s41586-025-08786-6 http://doi.org/10.1038/s41586-025-08786-6

S.R. Ahmed et al. , Universal photonic artificial intelligence acceleration . Nature 640 , 368 ( 2025 ). http://doi.org/10.1038/s41586-025-08854-x http://doi.org/10.1038/s41586-025-08854-x

A. Najjar Amiri , A.D. Vit , K. Gorgulu , E.S. Magden , Deep photonic network platform enabling arbitrary and broadband optical functionality . Nat. Commun. 15 , 1432 ( 2024 ). http://doi.org/10.1038/s41467-024-45846-3 http://doi.org/10.1038/s41467-024-45846-3

Y. Huang , W. Liu , R. Sun , T. Fu , Y. Wang , Z. Huang , S. Yang , H. Chen , Diffraction-driven parallel convolution processing with integrated photonics . Laser Photonics Rev. 19 , 2400972 ( 2025 ). http://doi.org/10.1002/lpor.202400972 http://doi.org/10.1002/lpor.202400972

Y. Shen et al. , Deep learning with coherent nanophotonic circuits . Nat. Photon 11 , 441 ( 2017 ). http://doi.org/10.1038/nphoton.2017.93 http://doi.org/10.1038/nphoton.2017.93

Z. Du et al. , Ultracompact and multifunctional integrated photonic platform . Sci. Adv. 10 , eadm7569 ( 2024 ). http://doi.org/10.1126/sciadv.adm7569 http://doi.org/10.1126/sciadv.adm7569

K. Liao et al. , Hetero-integrated perovskite/Si3N4 on-chip photonic system . Nat. Photon. 19 , 358 - 368 ( 2025 ). http://doi.org/10.1038/s41566-024-01603-y http://doi.org/10.1038/s41566-024-01603-y

K. Liao , C. Li , T. Dai , C. Zhong , H. Lin , X. Hu , Q. Gong , Matrix eigenvalue solver based on reconfigurable photonic neural network . Nanophotonics 11 , 4089 ( 2022 ). http://doi.org/10.1515/nanoph-2022-0109 http://doi.org/10.1515/nanoph-2022-0109

K. Liao et al. , All-optical computing based on convolutional neural networks . OEA 4 ,( 2021 ). http://doi.org/10.29026/oea.2021.200060 http://doi.org/10.29026/oea.2021.200060

S. Pai et al. , Experimentally realized in situ backpropagation for deep learning in photonic neural networks . Science 380 , 398 ( 2023 ). http://doi.org/10.1126/science.ade8450 http://doi.org/10.1126/science.ade8450

S. Bandyopadhyay , A. Sludds , S. Krastanov , R. Hamerly , N. Harris , D. Bunandar , M. Streshinsky , M. Hochberg , D. Englund , Single-chip photonic deep neural network with forward-only training . Nat. Photon. 18 , 1335 ( 2024 ). http://doi.org/10.1038/s41566-024-01567-z http://doi.org/10.1038/s41566-024-01567-z

T. Fu , Y. Zang , Y. Huang , Z. Du , H. Huang , C. Hu , M. Chen , S. Yang , H. Chen , Photonic machine learning with on-chip diffractive optics . Nat. Commun. 14 , 70 ( 2023 ). http://doi.org/10.1038/s41467-022-35772-7 http://doi.org/10.1038/s41467-022-35772-7

X. Wang , B. Redding , N. Karl , C. Long , Z. Zhu , J. Skowronek , S. Pang , D. Brady , R. Sarma , Integrated photonic encoder for low power and high-speed image processing . Nat. Commun. 15 , 4510 ( 2024 ). http://doi.org/10.1038/s41467-024-48099-2 http://doi.org/10.1038/s41467-024-48099-2

V. Nikkhah , A. Pirmoradi , F. Ashtiani , B. Edwards , F. Aflatouni , N. Engheta , Inverse-designed low-index-contrast structures on a silicon photonics platform for vector–matrix multiplication . Nat. Photon. 18 , 501 ( 2024 ). http://doi.org/10.1038/s41566-024-01394-2 http://doi.org/10.1038/s41566-024-01394-2

T. Wu , M. Menarini , Z. Gao , L. Feng , Lithography-free reconfigurable integrated photonic processor . Nat. Photon. 17 , 710 ( 2023 ). http://doi.org/10.1038/s41566-023-01205-0 http://doi.org/10.1038/s41566-023-01205-0

J. Cheng et al. , Multimodal deep learning using on-chip diffractive optics with in situ training capability . Nat. Commun. 15 , 6189 ( 2024 ). http://doi.org/10.1038/s41467-024-50677-3 http://doi.org/10.1038/s41467-024-50677-3

S. Ohno , R. Tang , K. Toprasertpong , S. Takagi , M. Takenaka , Si microring resonator crossbar array for on-chip inference and training of the optical neural network . ACS Photon. 9 , 2614 ( 2022 ). http://doi.org/10.1021/acsphotonics.1c01777 http://doi.org/10.1021/acsphotonics.1c01777

J. Cheng et al. , Human emotion recognition with a microcomb-enabled integrated optical neural network . Nanophotonics 12 , 3883 ( 2023 ). http://doi.org/10.1515/nanoph-2023-0298 http://doi.org/10.1515/nanoph-2023-0298

Z. Xu , T. Zhou , M. Ma , C. Deng , Q. Dai , L. Fang , Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence . Science 384 , 202 ( 2024 ). http://doi.org/10.1126/science.adl1203 http://doi.org/10.1126/science.adl1203

S. Hong et al. , Versatile parallel signal processing with a scalable silicon photonic chip . Nat. Commun. 16 , 288 ( 2025 ). http://doi.org/10.1038/s41467-024-55162-5 http://doi.org/10.1038/s41467-024-55162-5

D. Wang , Y. Nie , G. Hu , H.K. Tsang , C. Huang , Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS . Nat. Commun. 15 , 10841 ( 2024 ). http://doi.org/10.1038/s41467-024-55172-3 http://doi.org/10.1038/s41467-024-55172-3

B. Bai et al. , Microcomb-based integrated photonic processing unit . Nat. Commun. 14 , 66 ( 2023 ). http://doi.org/10.1038/s41467-022-35506-9 http://doi.org/10.1038/s41467-022-35506-9

H.H. Zhu et al. , Space-efficient optical computing with an integrated chip diffractive neural network . Nat. Commun. 13 , 1044 ( 2022 ). http://doi.org/10.1038/s41467-022-28702-0 http://doi.org/10.1038/s41467-022-28702-0

S. Xu , J. Wang , S. Yi , W. Zou , High-order tensor flow processing using integrated photonic circuits . Nat. Commun. 13 , 7970 ( 2022 ). http://doi.org/10.1038/s41467-022-35723-2 http://doi.org/10.1038/s41467-022-35723-2

S. Du , J. Zhang , H. Ouyang , Z. Tao , Q. Yan , H. Hao , J. Zhou , J. You , Y. Tang , T. Jiang , Scalable parallel photonic processing unit for various neural network accelerations . Photon. Res. 12 , 2691 ( 2024 ). http://doi.org/10.1364/PRJ.527940 http://doi.org/10.1364/PRJ.527940

A. Graves, J. Schmidhuber, Framewise phoneme classification with bi directional LSTM networks, In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005 , Vol. 4 (IEEE, Montreal, Que., Canada, 2005), pp. 2047–2052.

J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling , arXiv:1412.3555 http://arxiv.org/abs/1412.3555 .

J. Chen , S.-W. Jun , S. Hong , W. He , J. Moon , Eciton: very low-power recurrent neural network accelerator for real-time inference at the edge . ACM Trans. Reconfigur. Technol. Syst. 17 , 1 ( 2024 ). http://doi.org/10.1145/3695880 http://doi.org/10.1145/3695880

X. Zhang , Y. Zhu , X. Lou , Reconfigurable and energy-efficient architecture for deploying multi-layer RNNs on FPGA . IEEE Trans. Circ. Syst. I 71 , 5969 ( 2024 ).

G. Mourgias-Alexandris , G. Dabos , N. Passalis , A. Totovic , A. Tefas , N. Pleros , All-optical WDM recurrent neural networks with gating . IEEE J. Select. Topics Quantum Electron. 26 , 1 ( 2020 ). http://doi.org/10.1109/JSTQE.2020.2995830 http://doi.org/10.1109/JSTQE.2020.2995830

S. Becker , D. Englund , B. Stiller , An optoacoustic field-programmable perceptron for recurrent neural networks . Nat. Commun. 15 , 3020 ( 2024 ). http://doi.org/10.1038/s41467-024-47053-6 http://doi.org/10.1038/s41467-024-47053-6

J. Bueno , S. Maktoobi , L. Froehly , I. Fischer , M. Jacquot , L. Larger , D. Brunner , Reinforcement learning in a large-scale photonic recurrent neural network . Optica 5 , 756 ( 2018 ). http://doi.org/10.1364/OPTICA.5.000756 http://doi.org/10.1364/OPTICA.5.000756

T.W. Hughes , I.A.D. Williamson , M. Minkov , S. Fan , Wave physics as an analog recurrent neural network . Sci. Adv. 5 , eaay6946 ( 2019 ). http://doi.org/10.1126/sciadv.aay6946 http://doi.org/10.1126/sciadv.aay6946

B. Wang , T.F. De Lima , B.J. Shastri , P.R. Prucnal , C. Huang , Multi-wavelength photonic neuromorphic computing for intra and inter-channel distortion compensations in WDM optical communication systems . IEEE J. Select. Topics Quantum Electron. 29 , 1 ( 2023 ).

A.N. Tait , T.F. De Lima , E. Zhou , A.X. Wu , M.A. Nahmias , B.J. Shastri , P.R. Prucnal , Neuromorphic photonic networks using silicon photonic weight banks . Sci. Rep. 7 , 7430 ( 2017 ). http://doi.org/10.1038/s41598-017-07754-z http://doi.org/10.1038/s41598-017-07754-z

K. Vandoorne , P. Mechet , T. Van Vaerenbergh , M. Fiers , G. Morthier , D. Verstraeten , B. Schrauwen , J. Dambre , P. Bienstman , Experimental demonstration of reservoir computing on a silicon photonics chip . Nat. Commun. 5 , 3541 ( 2014 ). http://doi.org/10.1038/ncomms4541 http://doi.org/10.1038/ncomms4541

I. Bauwens , K. Harkhoe , E. Gooskens , P. Bienstman , G. Verschaffelt , G. Van Der Sande , Combining a passive spatial photonic reservoir computer with a semiconductor laser increases its nonlinear computational capacity . Opt. Express 32 , 24328 ( 2024 ). http://doi.org/10.1364/OE.518654 http://doi.org/10.1364/OE.518654

D. Zhong , P. Hou , J. Zhang , W. Deng , T. Wang , Y. Chen , Q. Wu , Excellent predictive-performances of photonic reservoir computers for chaotic time-series using the fusion-prediction approach . Opt. Express 31 , 24453 ( 2023 ). http://doi.org/10.1364/OE.491953 http://doi.org/10.1364/OE.491953

C. Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions, In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Boston, MA, USA, 2015), pp. 1–9.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Las Vegas, NV, USA, 2016), pp. 770–778.

Z. Ye et al. , Foundry manufacturing of tight-confinement, dispersion-engineered, ultralow-loss silicon nitride photonic integrated circuits . Photon. Res. 11 , 558 ( 2023 ). http://doi.org/10.1364/PRJ.486379 http://doi.org/10.1364/PRJ.486379

Y. Wang et al., Compact turnkey soliton microcombs at microwave rates via wafer-scale fabrication , arXiv:2502.10941 http://arxiv.org/abs/2502.10941 .

Li Deng , The MNIST database of handwritten digit images for machine learning research [Best of the Web] . IEEE Signal Process. Mag. 29 , 141 ( 2012 ). http://doi.org/10.1109/MSP.2012.2211477 http://doi.org/10.1109/MSP.2012.2211477

A. Krizhevsky, Learning multiple layers of features from tiny images (2009).

A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, C. Potts, learning word vectors for sentiment analysis, human language technologies , pp. 142–150 (2011).

K. Ito, L. J ohnson, The LJ Speech Dataset , https://keithito.com/LJ-Speech-Dataset/ https://keithito.com/LJ-Speech-Dataset/ .

B. Shen et al. , Integrated turnkey soliton microcombs . Nature 582 , 365 ( 2020 ). http://doi.org/10.1038/s41586-020-2358-x http://doi.org/10.1038/s41586-020-2358-x

H. Feng et al. , Integrated lithium niobate microwave photonic processing engine . Nature 627 , 80 ( 2024 ). http://doi.org/10.1038/s41586-024-07078-9 http://doi.org/10.1038/s41586-024-07078-9

Y. Hu et al., Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion , arXiv:2411.02734 http://arxiv.org/abs/2411.02734 .

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