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Multichannel meta-imagers for accelerating machine vision


Rapid developments in machine vision technology have impacted a variety of applications, such as medical devices and autonomous driving systems. These achievements, however, typically necessitate digital neural networks with the downside of heavy computational requirements and consequent high energy consumption. As a result, real-time decision-making is hindered when computational resources are not readily accessible. Here we report a meta-imager designed to work together with a digital back end to offload computationally expensive convolution operations into high-speed, low-power optics. In this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positively and negatively valued convolution operations in a single shot. We use our meta-imager for object classification, achieving 98.6% accuracy in handwritten digits and 88.8% accuracy in fashion images. Owing to its compactness, high speed and low power consumption, our approach could find a wide range of applications in artificial intelligence and machine vision applications.

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Fig. 1: Schematic of the meta-imager.
Fig. 2: Meta-optic architecture.
Fig. 3: Design of the meta-imager.
Fig. 4: Fabrication and characterization of the meta-imager.
Fig. 5: Classification of MNIST and Fashion MNIST objects.

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Data availability

The data that support the findings of this study are available in the Article and its Supplementary Information and/or are available from the corresponding author upon reasonable request.


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H.Z. and J.G.V. acknowledge support from DARPA under contract HR001118C0015 and NAVAIR under contract N6893622C0030. X.Z. acknowledges support from ONR under contract N000142112468. Y.H. and Q.L. acknowledge support from NIH under contract R01DK135597. Meta-optic devices were manufactured as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility, Oak Ridge National Laboratory.

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Authors and Affiliations



H.Z. and J.G.V. developed the idea. H.Z. conducted the optical modelling and system design. Q.L. and H.Z. trained the digital neural network. H.Z. fabricated the samples. I.I.K. performed the silicon growth and electron-beam-lithography for the metasurfaces. H.Z. conducted the experimental measurements. H.Z., Q.L. and X.Z. performed the data analysis. H.Z. and J.G.V. wrote the manuscript with input from all the authors. The project was supervised by Y.H. and J.G.V.

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Correspondence to Jason G. Valentine.

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Nature Nanotechnology thanks Junsuk Rho, Tianyu Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Zheng, H., Liu, Q., Kravchenko, I.I. et al. Multichannel meta-imagers for accelerating machine vision. Nat. Nanotechnol. 19, 471–478 (2024).

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