Highly scalable machine vision enabled with meta-optics-based ultra-wide neural network

Abstract Optical neural networks (ONNs) offer a route to low-latency, energy-efficient AI, but scaling them to modern model sizes is constrained by two practical bottlenecks: training large ONNs is computationally prohibitive, and implementing or tuning millions of optical components is highly sensitive to fabrication imperfections and alignment errors. Here we report a metasurface-based optical learning machine that bypasses these barriers by operating in an ultra-wide regime. We use an optical