Flat optics for analog computing: from fundamental mechanisms to advanced meta-processors

TL;DR

This review explores how optical metasurfaces enable ultra-compact, energy-efficient analog computing for visual data processing, highlighting fundamental mechanisms, recent advances, and future prospects.

Contribution

It provides a comprehensive overview of metasurface-based optical computing mechanisms and discusses recent innovations and future directions in the field.

Findings

Metasurface-empowered optical computing spans Fourier, nonlocal spatial, and interferometric architectures.

Recent breakthroughs include dynamic reconfigurability and nonlinear, quantum-assisted platforms.

Integration with AI-driven design and neural networks promises next-generation intelligent vision systems.

Abstract

As the explosive growth of visual data increasingly strains the latency and energy limits of conventional electronic computing, optical analog computing has re-emerged as a disruptive paradigm for zero-power, speed-of-light information processing. Propelled by the unprecedented wave-manipulation capabilities of optical metasurfaces, this field is undergoing a rapid transition from macroscopic physical optics to ultra-compact, on-chip meta-processors. This Review examines the fundamental mechanisms of metasurface-empowered optical computing spanning Fourier-domain, nonlocal spatial-domain, and interferometric architectures that perform mathematical operations, with a particular focus on spatial differentiation and edge detection as representative computing tasks. By emphasizing recent breakthroughs, we highlight the evolution of meta-processors from static, linear regimes to dynamically reconfigurable, nonlinear, and quantum-assisted multidimensional platforms. We also envision how the synergy of AI-driven inverse design and the integration of analog meta-front-ends with optical neural networks will synergistically revolutionize next-generation intelligent machine vision.