Single-layer spatial analog meta-processor for imaging processing

Here, the authors propose a Fourier-based metaprocessor to impart customized highly flexible transfer functions for analog computing. Differentiation and cross-correlation are performed to substantiate the ultracompact and high-throughput kernel processor. Computational meta-optics brings a twist on the accelerating hardware with the benefits of ultrafast speed, ultra-low power consumption, and parallel information processing in versatile applications. Recent advent of metasurfaces have enabled the full manipulation of electromagnetic waves within subwavelength scales, promising the multifunctional, high-throughput, compact and flat optical processors. In this trend, metasurfaces with nonlocality or multi-layer structures are proposed to perform analog optical computations based on Green's function or Fourier transform, intrinsically constrained by limited operations or large footprints/volume. Here, we showcase a Fourier-based metaprocessor to impart customized highly flexible transfer functions for analog computing upon our single-layer Huygens' metasurface. Basic mathematical operations, including differentiation and cross-correlation, are performed by directly modulating complex wavefronts in spatial Fourier domain, facilitating edge detection and pattern recognition of various image processing. Our work substantiates an ultracompact and powerful kernel processor, which could find important applications for optical analog computing and image processing.

Automated summary

The authors propose a Fourier-based metaprocessor that uses complex wavefront modulation in spatial Fourier domain to perform basic mathematical operations for analog computing. This metaprocessor has the advantages of being ultracompact and powerful, making it suitable for applications in optical analog computing and image processing.