Compact, reconfigurable, and scalable photonic neurons by modulation-and-weighting microring resonators

Weipeng Zhang; Yuxin Wang; Joshua C. Lederman; Bhavin J. Shastri; Paul R. Prucnal

doi:10.1186/s43593-026-00122-3

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Compact, reconfigurable, and scalable photonic neurons by modulation-and-weighting microring resonators

eLight Vol. 6, (2026)

Research Article | Updated：2026-03-16

- Compact, reconfigurable, and scalable photonic neurons by modulation-and-weighting microring resonators
- Affiliations：
- Author bio：
- Funds：
- DOI：10.1186/s43593-026-00122-3
  CLC：
- Received：03 September 2025，
  
  Revised：2026-01-05，
  
  Accepted：12 January 2026，
  
  Online First：09 February 2026，
  
  Published：2026-12
- Accepted：
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Weipeng Zhang, Yuxin Wang, Joshua C. Lederman, et al. Compact, reconfigurable, and scalable photonic neurons by modulation-and-weighting microring resonators[J]. eLight, 2026, 6.
DOI：

Weipeng Zhang, Yuxin Wang, Joshua C. Lederman, et al. Compact, reconfigurable, and scalable photonic neurons by modulation-and-weighting microring resonators[J]. eLight, 2026, 6. DOI： 10.1186/s43593-026-00122-3.

摘要

Abstract

Neuromorphic photonics promises sub-nanosecond latency

ultrawide bandwidth

and high parallelism

but practical scalability is constrained by fabrication tolerances

spectral alignment

and tuning energy. Here

we present a large-scale

compact

and reconfigurable photonic neuron in which each microring performs modulation and weighting simultaneously. By exploiting both carrier and thermal tuning within a single device

this architecture reduces footprint

relaxes spectral alignment requirements to just two optical components

and yields a steep transfer response that lowers tuning energy. The proposed neuron supports multiple operating configurations

allowing its dynamical behavior to be adapted to different computational tasks. In particular

a short electrical feedback path enables recurrent operation

providing tunable short- and long-term memory for temporal processing. Using a 10-microring resonator ar

ray

we demonstrate both spatial and temporal computing

including a 3

$$$\times$$$

3 convolution for image processing with an error of

5% and high-frequency financial time-series prediction. Each modulation-weighting element occupies 80

$$$\times$$$

$$$\upmu$$$

$$$^2$$$

and consumes an average of 0.186 mW

corresponding to a compute density of 4.67 TOPS/

s/mm

$$$^2$$$

. Excluding electronic power

the on-chip tuning efficiency reaches approximately 105 TOPs/W

which is comparable to state-of-the-art implementations. These results indicate that modulation-and-weighting microring resonator banks provide a scalable building block for large-scale neuromorphic photonic systems

offering a favorable combination of compact footprint

low power consumption

and functional flexibility.

关键词

Keywords

references

D. Brunner et al., Roadmap on neuromorphic photonics. Preprint at https://arxiv.org/abs/2501.07917 https://arxiv.org/abs/2501.07917 (2025)

W. Zhang et al. , A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency . Light Sci. Appl. 13 , 14 ( 2024 ). http://doi.org/10.1038/s41377-023-01362-5 http://doi.org/10.1038/s41377-023-01362-5

S. Shekhar et al. , Roadmapping the next generation of silicon photonics . Nat. Commun. 15 , 751 ( 2024 ). http://doi.org/10.1038/s41467-024-44750-0 http://doi.org/10.1038/s41467-024-44750-0

C. Huang et al. , Prospects and applications of photonic neural networks . Adv. Phys. X 7 , 1981155 ( 2022 ).

T. Fu et al. , Optical neural networks: progress and challenges . Light Sci. Appl. 13 , 263 ( 2024 ). http://doi.org/10.1038/s41377-024-01590-3 http://doi.org/10.1038/s41377-024-01590-3

X. Xu , X. Jin , Integrated photonic computing beyond the von neumann architecture . ACS Photon. 10 , 1027 - 1036 ( 2023 ).

T. Ferreira De Lima , B.J. Shastri , A.N. Tait , M.A. Nahmias , P.R. Prucnal , Progress in neuromorphic photonics . Nanophotonics 6 , 577 - 599 ( 2017 ). http://doi.org/10.1515/nanoph-2016-0139 http://doi.org/10.1515/nanoph-2016-0139

T. Xu et al. , Control-free and efficient integrated photonic neural networks via hardware-aware training and pruning . Optica 11 , 1039 - 1049 ( 2024 ). http://doi.org/10.1364/OPTICA.523225 http://doi.org/10.1364/OPTICA.523225

E.A. Doris et al. , Design automation of photonic resonator weights . Nanophotonics 11 , 3805 - 3822 ( 2022 ). http://doi.org/10.1515/nanoph-2022-0049 http://doi.org/10.1515/nanoph-2022-0049

W. Bogaerts , L. Chrostowski , Silicon photonics circuit design: methods, tools and challenges . Laser Photon. Rev. 12 , 1700237 ( 2018 ). http://doi.org/10.1002/lpor.201700237 http://doi.org/10.1002/lpor.201700237

A.N. Tait et al. , 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

W. Bogaerts , M. Fiers , P. Dumon , Design challenges in silicon photonics . IEEE JSTQE 20 , 1 - 8 ( 2013 ).

W. Zhang et al. , Silicon microring synapses enable photonic deep learning beyond 9-bit precision . Optica 9 , 579 - 584 ( 2022 ). http://doi.org/10.1364/OPTICA.446100 http://doi.org/10.1364/OPTICA.446100

A.N. Tait et al. , Microring weight banks. IEEE . JSTQE 22 , 312 - 325 ( 2016 ).

W. Bogaerts et al. , Silicon microring resonators . Laser Photon. Rev. 6 , 47 - 73 ( 2012 ). http://doi.org/10.1002/lpor.201100017 http://doi.org/10.1002/lpor.201100017

A. Vaswani, Attention is all you need. Paper presented at 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, 4-9 Dec 2017

J. Kaplan et al., Scaling laws for neural language models. Preprint at https://arxiv.org/abs/2001.08361 https://arxiv.org/abs/2001.08361 (2020)

C. Huang et al. , Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits . APL Photon. 5 , 4 ( 2020 ). http://doi.org/10.1063/1.5144121 http://doi.org/10.1063/1.5144121

W. Zhang, J. Zhang, J.C. Lederman, B.J. Shastri, and P. Prucnal, Microring modulation-and-weight banks. Paper presented at 44th Conference on Lasers and Electro-Optics (CLEO), Charlotte, 5-10 May 2024

Z. Li , F. Liu , W. Yang , S. Peng , J. Zhou , A survey of convolutional neural networks: analysis, applications, and prospects . IEEE Trans. Neural Netw. Learn. Syst. 33 , 6999 - 7019 ( 2021 ). http://doi.org/10.1109/TNNLS.2021.3084827 http://doi.org/10.1109/TNNLS.2021.3084827

X. Xu et al. , 11 tops photonic convolutional accelerator for optical neural networks . Nature 589 , 44 - 51 ( 2021 ). http://doi.org/10.1038/s41586-020-03063-0 http://doi.org/10.1038/s41586-020-03063-0

A.N. Tait et al. , Silicon photonic modulator neuron . Phys. Rev. Appl. 11 ,( 2019 ). http://doi.org/10.1103/PhysRevApplied.11.064043 http://doi.org/10.1103/PhysRevApplied.11.064043

A.N. Tait , Quantifying power in silicon photonic neural networks . Phys. Rev. Appl. 17 ,( 2022 ). http://doi.org/10.1103/PhysRevApplied.17.054029 http://doi.org/10.1103/PhysRevApplied.17.054029

V.K. Narayana , S. Sun , A.-H.A. Badawy , V.J. Sorger , T. El-Ghazawi , Morphonoc: Exploring the design space of a configurable hybrid noc using nanophotonics . Microprocess. Microsyst. 50 , 113 - 126 ( 2017 ). http://doi.org/10.1016/j.micpro.2017.03.006 http://doi.org/10.1016/j.micpro.2017.03.006

B. Dong et al. , Higher-dimensional processing using a photonic tensor core with continuous-time data . Nat. Photon. 17 , 1080 - 1088 ( 2023 ). http://doi.org/10.1038/s41566-023-01313-x http://doi.org/10.1038/s41566-023-01313-x

J.C. Lederman et al. , Real-time photonic blind interference cancellation . Nat. Commun. 14 , 8197 ( 2023 ). http://doi.org/10.1038/s41467-023-43982-w http://doi.org/10.1038/s41467-023-43982-w

W. Zhang et al. , Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation . Nat. Commun. 14 , 1107 ( 2023 ). http://doi.org/10.1038/s41467-023-36814-4 http://doi.org/10.1038/s41467-023-36814-4

III R. Davis , Z. Chen , R. Hamerly , D. Englund , RF-photonic deep learning processor with Shannon-limited data movement . Sci. Adv. 11 , 3558 ( 2025 ). http://doi.org/10.1126/sciadv.adt3558 http://doi.org/10.1126/sciadv.adt3558

Y. Huang et al. , Feature extraction from images using integrated photonic convolutional kernel . IEEE Photon. J. 14 , 1 - 7 ( 2022 ).

S. Pai et al. , Experimentally realized in situ backpropagation for deep learning in photonic neural networks . Science 380 , 398 - 404 ( 2023 ). http://doi.org/10.1126/science.ade8450 http://doi.org/10.1126/science.ade8450

Y. Shen et al. , Deep learning with coherent nanophotonic circuits . Nat. Photon. 11 , 441 - 446 ( 2017 ). http://doi.org/10.1038/nphoton.2017.93 http://doi.org/10.1038/nphoton.2017.93

N.A. Aadit et al. , Massively parallel probabilistic computing with sparse ising machines . Nat. Electron. 5 , 460 - 468 ( 2022 ). http://doi.org/10.1038/s41928-022-00774-2 http://doi.org/10.1038/s41928-022-00774-2

D. MacKenzie , Trading at the speed of light: How ultrafast algorithms are transforming financial markets 1 - 304 ( Princeton , Princeton University Press , 2021 ).

M. Buchanan , Physics in finance: Trading at the speed of light . Nature 518 , 161 - 163 ( 2015 ). http://doi.org/10.1038/518161a http://doi.org/10.1038/518161a

J. Bacidore , R.H. Battalio , R.H. Jennings , Order submission strategies, liquidity supply, and trading in pennies on the new york stock exchange . J. Financ. Mark. 6 , 337 - 362 ( 2003 ). http://doi.org/10.1016/S1386-4181(03)00003-X http://doi.org/10.1016/S1386-4181(03)00003-X

J. Hasbrouck , G. Saar , Low-latency trading . J. Financ. Mark. 16 , 646 - 679 ( 2013 ). http://doi.org/10.1016/j.finmar.2013.05.003 http://doi.org/10.1016/j.finmar.2013.05.003

A.J. Menkveld , M.A. Zoican , Need for speed? exchange latency and liquidity . Rev. Financ. Stud. 30 , 1188 - 1228 ( 2017 ). http://doi.org/10.1093/rfs/hhx006 http://doi.org/10.1093/rfs/hhx006

K.Z. Zaharudin , M.R. Young , W.-H. Hsu , High-frequency trading: Definition, implications, and controversies . J. Econ. Surv. 36 , 75 - 107 ( 2022 ). http://doi.org/10.1111/joes.12434 http://doi.org/10.1111/joes.12434

R. Garvey , F. Wu , Speed, distance, and electronic trading: New evidence on why location matters . J. Financ. Mark. 13 , 367 - 396 ( 2010 ). http://doi.org/10.1016/j.finmar.2010.07.001 http://doi.org/10.1016/j.finmar.2010.07.001

H. Subramoni, F. Petrini, V. Agarwal, and D. Pasetto, Streaming, low-latency communication in on-line trading systems. Paper presented at 24th IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), Georgia, 19-23 Apr 2010

J. Sandoval, High-frequency trading strategy based on deep neural networks, Paper presented at 12th International conference on intelligent computing (ICIC), Lanzhou, 2-5 Aug 2016

M. Kearns and Y. Nevmyvaka, in Machine learning for market microstructure and high frequency trading, ed. by M. O’hara, M. López de Prado and D. Easley. High Frequency Trading: New Realities for Traders, Markets, and Regulators , vol 72 (Risk Books, London, 2013) p. 1877

J. Kennedy and R. Eberhart, Particle swarm optimization. Paper presented at the International conference on neural networks (ICNN), Perth, 27-31 Nov 1995

J. Zhang et al. , Online training and pruning of multi-wavelength photonic neural networks . Nanophotonics 14 , 5035 - 5046 ( 2025 ). http://doi.org/10.1515/nanoph-2025-0296 http://doi.org/10.1515/nanoph-2025-0296

J. Feldmann et al. , Parallel convolutional processing using an integrated photonic tensor core . Nature 589 , 52 - 58 ( 2021 ). http://doi.org/10.1038/s41586-020-03070-1 http://doi.org/10.1038/s41586-020-03070-1

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

X. Meng et al. , Compact optical convolution processing unit based on multimode interference . Nat. Commun. 14 , 3000 ( 2023 ). http://doi.org/10.1038/s41467-023-38786-x http://doi.org/10.1038/s41467-023-38786-x

Y. Zheng et al. , Photonic neural network fabricated on thin film lithium niobate for high-fidelity and power-efficient matrix computation . Laser Photon. Rev. 18 , 2400565 ( 2024 ). http://doi.org/10.1002/lpor.202400565 http://doi.org/10.1002/lpor.202400565

Y. Xie et al. , Complex-valued matrix-vector multiplication using a scalable coherent photonic processor . Sci. Adv. 11 , 7475 ( 2025 ). http://doi.org/10.1126/sciadv.ads7475 http://doi.org/10.1126/sciadv.ads7475

J. He et al., Programmable electro-optic frequency comb empowers integrated parallel convolution processing. Preprint at https://arxiv.org/abs/2506.18310 https://arxiv.org/abs/2506.18310 (2025)

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