Spatiotemporal Computational Meta‐Imager for Broadband Dual‐Layer Optical Convolution

ABSTRACT Optical neural networks face a tradeoff between device footprint and computational expressivity: deep systems are bulky, whereas single‐layer hardware lacks intrinsic depth and native negative‐weight support. We experimentally demonstrate a single‐layer metalens imager with “spatiotemporal intrinsic depth” for equivalent broadband dual‐layer convolution. A polarization‐multiplexed metasurface provides fixed Bessel‐vortex channels ( l = 0 to 3) as the immutable first convolution layer, where the ( l = 0) channel implements a hardware‐native residual connection. A metasurface‐generated 3 × 3 optical vortex matrix forms the second functional layer; time‐sequential liquid‐crystal amplitude modulation delivers tunable positive/negative weights. This temporal encoding solves the optical sign problem and converts fabrication imperfections into compensable biases via in‐situ training. Experiments verify rotational robustness for fashion‐item classification, raise full‐color CIFAR‐10 accuracy from 57.5 % to 86.6 %, and reach 99.3 % on the SIPaKMeD medical dataset, offloading 99.7 % of convolutional multiply‐accumulate operations to optics. By removing volumetric diffractive layers, this work enables low‐latency hybrid optoelectronic edge intelligent vision.