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Integrated lithium niobate microwave photonic processing engine

Nature volume 627, pages 80–87 (2024)Cite this article

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Abstract

Integrated microwave photonics (MWP) is an intriguing technology for the generation, transmission and manipulation of microwave signals in chip-scale optical systems1,2. In particular, ultrafast processing of analogue signals in the optical domain with high fidelity and low latency could enable a variety of applications such as MWP filters3,4,5, microwave signal processing6,7,8,9 and image recognition10,11. An ideal integrated MWP processing platform should have both an efficient and high-speed electro-optic modulation block to faithfully perform microwave–optic conversion at low power and also a low-loss functional photonic network to implement various signal-processing tasks. Moreover, large-scale, low-cost manufacturability is required to monolithically integrate the two building blocks on the same chip. Here we demonstrate such an integrated MWP processing engine based on a 4 inch wafer-scale thin-film lithium niobate platform. It can perform multipurpose tasks with processing bandwidths of up to 67 GHz at complementary metal–oxide–semiconductor (CMOS)-compatible voltages. We achieve ultrafast analogue computation, namely temporal integration and differentiation, at sampling rates of up to 256 giga samples per second, and deploy these functions to showcase three proof-of-concept applications: solving ordinary differential equations, generating ultra-wideband signals and detecting edges in images. We further leverage the image edge detector to realize a photonic-assisted image segmentation model that can effectively outline the boundaries of melanoma lesion in medical diagnostic images. Our ultrafast lithium niobate MWP engine could provide compact, low-latency and cost-effective solutions for future wireless communications, high-resolution radar and photonic artificial intelligence.

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Fig. 1: Wafer-scale LN-based MWP signal-processing engine and its building blocks.
Fig. 2: High-speed MWP temporal integrator.
Fig. 3: High-speed MWP temporal differentiator.
Fig. 4: High-speed photonic-assisted medical image segmentation.

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Data availability

The data used to produce the plots within this paper are available from Zenodo at https://doi.org/10.5281/zenodo.10464317 (ref. 66).

Code availability

The code used to produce the plots within this paper is available from Zenodo at https://doi.org/10.5281/zenodo.10463902 (ref. 67).

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Acknowledgements

We thank H. K. Tsang for the use of the high-speed measurement equipment. We thank C. F. Yeung, S. Y. Lao, C. W. Lai and L. Ho at the Nanosystem Fabrication Facility at the Hong Kong University of Science and Technology for technical support with the stepper lithography and plasma-enhanced chemical vapour deposition process. We thank W. H. Wong and K. Shum at CityU for their help in device fabrication and measurement. This work is supported by the National Natural Science Foundation of China (grant no. 61922092), the Research Grants Council, University Grants Committee (grant nos. CityU 11204820, CityU 21208219, N_CityU113/20 and C1002-22Y), the Croucher Foundation (grant no. 9509005), the Innovation and Technology Fund (grant no. ITS/226/21FP) and the City University of Hong Kong (grant nos. 9610402 and 9610455).

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Author notes

  1. These authors contributed equally: Hanke Feng, Tong Ge

Authors and Affiliations

  1. Department of Electrical Engineering & State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon, China

    Hanke Feng, Tong Ge, Xiaoqing Guo, Yiwen Zhang, Zhaoxi Chen, Sha Zhu, Ke Zhang, Wenzhao Sun, Yixuan Yuan & Cheng Wang

  2. Department of Engineering Science, University of Oxford, Oxford, UK

    Xiaoqing Guo

  3. Department of Electronic Engineering, Chinese University of Hong Kong, Shatin, China

    Benshan Wang, Chaoran Huang & Yixuan Yuan

  4. College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Sha Zhu

  5. City University of Hong Kong (Dongguan), Dongguan, China

    Wenzhao Sun

  6. Center of Information and Communication Technology, City University of Hong Kong Shenzhen Research Institute, Shenzhen, China

    Wenzhao Sun

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Contributions

H.F. and C.W. conceived the idea. H.F. designed and fabricated the wafer with the help of Z.C., Y.Z., K.Z. and W.S. T.G. and X.G. performed the numerical simulations. H.F., T.G. and B.W. carried out the high-speed measurements and analysed the data with the help of X.G., S.Z. and Y.Z. H.F. prepared the manuscript with contributions from all authors. C.H., Y.Y. and C.W. supervised the project. H.F. and T.G. contributed equally to this work.

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Correspondence to Cheng Wang.

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Nature thanks Jose Azana, Andreas Boes and David Marpaung for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Analog signal processing bandwidth analysis based on sawtooth signal.

a Time- and frequency- domain representations of sawtooth signal. b Analog bandwidth limitations of various components in our experiment. c Measured temporal differentiation results (solid) of sawtooth signals at fundamental frequencies of 20, 22, and 24 GHz, in comparison with simulated data (dashed) taking into consideration the first three harmonics. d Measured optical spectra of the differentiated signals, in comparison with the simulated results (red). e Measured (blue dots) optical power of the third-harmonic component of differentiated sawtooth signals at frequencies from 57 to 72 GHz, together with inferred on-chip performances after de-embedding equipment responses (red circles) and simulation results (red curve). Osc., oscilloscope. Lim., limitation. Sim., simulation.

Extended Data Fig. 2 Error analysis under various practical limitations.

a–d, Simulated output signals (red) are shown in comparison with the ideal response (blue) when considering the limitation of equipment bandwidth (a), drifting of device operation point (b), as well as the processing bandwidth limitations of the MZI-based differentiator (c) and the ring-based integrator (d). F.R., frequency response of the devices.

Extended Data Fig. 3 Simulated and measured mean absolute errors as functions of the cut-off frequency of Sinc pulse for the temporal differentiator

.

Extended Data Fig. 4 Edge-detection results of a representative melanoma lesion image.

a. Raw image. b–d. Edge-extracted images using convolution (b) and simple differentiation (c) algorithms in electronic computers, and our photonic-assisted edge detector (d). e. Ground truth.

Extended Data Table 1 Performance comparison with traditional electronics-based algorithms
Full size table
Extended Data Table 2 Estimated energy consumption of the photonic-assisted image edge detector
Full size table
Extended Data Table 3 Performance comparison with previous MWP demonstrations
Full size table

Supplementary information

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Feng, H., Ge, T., Guo, X. et al. Integrated lithium niobate microwave photonic processing engine. Nature 627, 80–87 (2024). https://doi.org/10.1038/s41586-024-07078-9

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