As the depth of a machine learning system outweighs the number of elements per layer, here we demonstrate high accuracy image classifications at telecommunication wavelengths in the multi-layer one-dimensional metasurface systems, with CMOS foundry compatible process. Arrays of rectangular slots are defined in the silicon layer. The slot lengths in those phase-only transmissive arrays are pre-trained by deep diffractive neuron networks. Beyond conventional classification functions, the metasystems also demonstrate unique functions of wavelength demultiplexing and multi-wavelength pattern classifications, with potential applications from spatial division multiplexing based optical interconnects to machine vision.

The new way of creating photonic integrated circuits (PIC) with cascaded metasurface layers, named metasystem, offers the possibility of integration thousands of programmed phase shifters in a sub mm area. The manifested integration scale brings the intelligence to the PIC - which can handle errors and uncertainties in the inputs, and perform the 'hyperspecial image classifications'. 

Fig. 1: Integrated metasurface system for spatial pattern classification. 

Wang, Z., Chang, L., Wang, F. et al. Integrated photonic metasystem for image classifications at telecommunication wavelength. Nat Commun 13, 2131 (2022).

https://doi.org/10.1038/s41467-022-29856-7