  Recent years have seen a considerable surge of research on developing
heuristic approaches to realize analog computing using physical waves. Among
these, neuromorphic computing using light waves is envisioned to feature
performance metrics such as computational speed and energy efficiency exceeding
those of conventional digital techniques by many orders of magnitude. Yet,
neuromorphic computing based on photonics remains a challenge due to the
difficulty of training and manufacturing sophisticated photonic structures to
support neural networks with adequate expressive power. Here, we realize a
diffractive optical neural network (ONN) based on metasurfaces that can
recognize objects by directly processing light waves scattered from the
objects. Metasurfaces composed of a two-dimensional array of millions of
meta-units can realize precise control of optical wavefront with subwavelength
resolution; thus, when used as constitutive layers of an ONN, they can provide
exceptionally high expressive power. We experimentally demonstrate ONNs based
on single-layered metasurfaces that modulate the phase and polarization over
optical wavefront for recognizing optically coherent binary objects, including
hand-written digits and English alphabetic letters. We further demonstrate, in
simulation, ONNs based on metasurface doublets for human facial verification.
The advantageous traits of metasurface-based ONNs, including ultra-compact form
factors, zero power consumption, ultra-fast and parallel data processing
capabilities, and physics-guaranteed data security, make them suitable as
"edge" perception devices that can transform the future of image collection and
analysis.
