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

Mingcheng Luo; Meirui Jiang; Bhavin J. Shastri; Nansen Zhou; Wenfei Guo; Jianmin Xiong; Dongliang Wang; Renjie Zhou; Chester Shu; Qi Dou; Chaoran Huang

doi:10.1186/s43593-026-00127-y

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

eLight Vol. 6, (2026)

Research Article | Updated：2026-07-21

- Highly scalable machine vision enabled with meta-optics-based ultra-wide neural network
- Affiliations：
- Author bio：
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- DOI：10.1186/s43593-026-00127-y
  CLC：
- Received：23 October 2025，
  
  Revised：2026-02-10，
  
  Accepted：11 February 2026，
  
  Online First：18 March 2026，
  
  Published：2026-12
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Mingcheng Luo, Meirui Jiang, Bhavin J. Shastri, et al. Highly scalable machine vision enabled with meta-optics-based ultra-wide neural network[J]. eLight, 2026, 6.
DOI：

Mingcheng Luo, Meirui Jiang, Bhavin J. Shastri, et al. Highly scalable machine vision enabled with meta-optics-based ultra-wide neural network[J]. eLight, 2026, 6. DOI： 10.1186/s43593-026-00127-y.

摘要

Abstract

Optical neural networks (ONNs) offer a route to low-latency

energy-efficient AI

but scaling them to modern model sizes is constra

ined 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 metasurface with 41 million optical parameters to form an ultra-wide NN

and show that

at this scale

a fixed

untrained metasurface can closely approximate a fully trained one. Residual mismatch is compensated by a compact digital backend with only

$$$10^2$$$

–

$$$10^4$$$

trainable parameters

enabling task adaptation without retraining or re-fabricating the metasurface. Experimentally

the resulting hybrid system delivers scalable machine vision across six tasks

achieving accuracy competitive with state-of-the-art models including ResNet and Vision Transformer

while remaining tolerant to fabrication imperfections and misalignment. Our approach is highly scalable

tolerant to fabrication errors

and operates under both coherent and incoherent illumination

thus providing a practical pathway t

o large-scale

high-performance optical AI computing.

关键词

Keywords

references

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