Fig. 3 |. Design of the meta-imager.

a, Design process of the hybrid neural network. A shallow convolutional neural network was trained at first. In this case, the input is convoluted by 12 independent channels, each comprising 7 × 7 pixel kernels. The convolution operations are implemented using the meta-imager, with the extracted feature maps, including multiplexed polarization channels, recorded by a polarization-sensitive camera (polarsens cam). The processed feature maps were then fed into the pretrained digital neural network to obtain the probability histogram for image classification. The percentage of relevant computing operations is indicated in the corner. ReLU, rectified linear unit; FC, fully connected. b, Schematic of the meta-atoms for the first (MS1) and second (MS2) metasurfaces. The height is fixed at 0.63 μm, whereas the lattice constant is chosen as 0.45 and 0.47 μm, respectively.