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Frequency–angular resolving LiDAR using chip-scale acousto-optic beam steering

Nature volume 620, pages 316–322 (2023)Cite this article

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Abstract

Thanks to its superior imaging resolution and range, light detection and ranging (LiDAR) is fast becoming an indispensable optical perception technology for intelligent automation systems including autonomous vehicles and robotics1,2,3. The development of next-generation LiDAR systems critically needs a non-mechanical beam-steering system that scans the laser beam in space. Various beam-steering technologies4 have been developed, including optical phased array5,6,7,8, spatial light modulation9,10,11, focal plane switch array12,13, dispersive frequency comb14,15 and spectro-temporal modulation16. However, many of these systems continue to be bulky, fragile and expensive. Here we report an on-chip, acousto-optic beam-steering technique that uses only a single gigahertz acoustic transducer to steer light beams into free space. Exploiting the physics of Brillouin scattering17,18, in which beams steered at different angles are labelled with unique frequency shifts, this technique uses a single coherent receiver to resolve the angular position of an object in the frequency domain, and enables frequency–angular resolving LiDAR. We demonstrate a simple device construction, control system for beam steering and frequency domain detection scheme. The system achieves frequency-modulated continuous-wave ranging with an 18° field of view, 0.12° angular resolution and a ranging distance up to 115 m. The demonstration can be scaled up to an array realizing miniature, low-cost frequency–angular resolving LiDAR imaging systems with a wide two-dimensional field of view. This development represents a step towards the widespread use of LiDAR in automation, navigation and robotics.

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Fig. 1: Principle of FAR LiDAR based on AOBS and chip-scale devices.
Fig. 2: Characterization of AOBS and multi-beam generation.
Fig. 3: 2D FAR LiDAR imaging.
Fig. 4: 3D imaging by FMCW and FAR LiDAR.

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Data availability

Source data are provided with this paper.

Code availability

No custom computer code or mathematical algorithm was used to generate the results that are reported in this study.

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Acknowledgements

This work is supported by the Convergence Accelerator programme of the National Science Foundation (award no. ITE-2134345) and the DARPA MTO SOAR programme. Part of this work was conducted at the Washington Nanofabrication Facility/Molecular Analysis Facility, a National Nanotechnology Coordinated Infrastructure (NNCI) site at the University of Washington with partial support from the National Science Foundation via award nos. NNCI-1542101 and NNCI-2025489.

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Author notes

  1. These authors contributed equally: Bingzhao Li, Qixuan Lin

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA

    Bingzhao Li, Qixuan Lin & Mo Li

  2. Department of Physics, University of Washington, Seattle, WA, USA

    Mo Li

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Contributions

M.L. conceived and supervised the research. B.L. and Q.L. designed and fabricated the devices, performed the experiments, conducted the simulation and derived the theory. All authors jointly analysed the data and wrote the manuscript.

Corresponding author

Correspondence to Mo Li.

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Competing interests

B.L., Q.L. and M.L. have filed a patent application (application serial number PCT/US2023/061401) on the FAR LiDAR by AOBS technology presented in this paper. M.L. has filed one US patent application (application number 17/602,415) on the integrated AOBS technology presented in this paper.

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Nature thanks Linbo Shao, Kan Wu and the other, anonymous, reviewer(s) 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 Acoustic mode characteristics.

a, The RF reflection coefficient S11 and the electromechanical conversion efficiency of the chirped IDT based on the modified Butterworh-Van Dyke (mBVD) model. The red shaded area marks the Rayleigh mode acoustic wave that is responsible for acousto-optic beam steering in our device and has an electromechanical conversion efficiency of ~6.4% within a bandwidth of 350 MHz. The grey shaded area marks the bands of multiple bulk modes that are inefficient in acousto-optic beam steering because they are largely confined in the SiO2 layer, although more efficiently excited. b, Simulation of the Rayleigh mode acoustic wave, showing the mode is confined in the LN layer.

Source data

Extended Data Fig. 2 Beam profile analysis.

a, k-space beam profile (gray circles) and their Lorentzian fit (orange line) at two different steering angles. b, Experimental (gray circle) and theoretical (orange line) results of the frequency-angle relation of the AOBS device. The theoretical results were calculated using the SAW dispersion and TE0 mode effective index simulated by COMSOL, in excellent agreement with the experimental results. c, Fitted beam width (FWHM) of the measured beam spots at different angles (blue circles) and theoretical beam width (orange line), showing an average divergence angle of 0.123°±0.022°.

Source data

Extended Data Fig. 3 AOBS dynamic response.

The temporal response of one AOBS pixel was characterized by measuring the output beam optical power (b) when the RF drive signal was modulated by a square wave (a). The 20%–80% rise time is measured to be 1.53 μs.

Source data

Extended Data Fig. 4 3D imaging of an object.

a, A 3D FMCW and FAR LiDAR pointcloud image of a letter “W” cutout made of a retroreflective film and placed 1.8 meters away. The frequency chirp excursion is increased to fE = 10 GHz to improve the depth measurement resolution. b, Photograph of the “W” cutout, which is 4.6 cm wide and 5.0 cm tall. The cutout is tilted to demonstrate the depth measurement. c, X-Z plane projection of the captured image, showing a depth measurement resolution (standard deviation, shaded area) of 7.5 mm. The variation of the standard deviation is due to the finite spot size (3.0 mm horizontally and 3.8 mm vertically) on the target and the geometry of the target.

Source data

Extended Data Fig. 5 Demonstration of longer range LiDAR Imaging.

a. FAR LiDAR signal from a target at varying distances between 2-12 meters. The SNR remains >20 dB in the range. b. Photo of our laboratory LiDAR demonstration, where targets as far as ~12 meters away are imaged by our LiDAR system. c. LiDAR signal SNR dependence on the distance R and fitted with 1/R2. Extrapolating to 3 dB SNR shows a measurement distance up to 115 meters. d. and e. 3D LiDAR imaging of a circular target (a driveway marker) and a triangular target (a retroreflective tape cut-out) at 8.55 m.

Extended Data Fig. 6 Schematics of optical characterization setups.

a, 4f system directly imaging the real-space beam profile at the emission aperture. b, Setup for imaging the k-space beam profile.

Source data

Supplementary information

Supplementary Information

Supplementary Sections 1–9, including Figs. 1–10 and references; see Table of Contents for details.

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Source data

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Li, B., Lin, Q. & Li, M. Frequency–angular resolving LiDAR using chip-scale acousto-optic beam steering. Nature 620, 316–322 (2023). https://doi.org/10.1038/s41586-023-06201-6

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