The increasing demands in intelligent decision-making, autonomous driving, and public security have led to a dramatic rise in the need for rapid optical image perception and processing. The current approach of sampling before processing has created substantial pressure on backend electronic computing systems due to data proliferation. Electronic computing faces inherent limitations due to electron migration rates, creating a speed bottleneck. Furthermore, the Joule heating generated during electron movement presents a significant constraint on computational advancement. Optical analog computing frontends that combine optical perception with computation present a viable alternative. These systems facilitate ultra-high-speed, high-bandwidth, low-loss, two-dimensional parallel all-optical operations for frontend data preprocessing, potentially reducing photoelectric conversion device requirements while easing backend processing loads. Nevertheless, traditional optical analog computing systems face limitations regarding their physical size, dimensional constraints, and parallel processing capabilities.

Metasurfaces, artificial materials that manipulate multidimensional light fields at subwavelength scales, offer a promising solution. These structures can independently and flexibly modulate multiple dimensional parameters including amplitude, phase, and polarization of the optical wavefront. Metasurfaces demonstrate considerable advantages over traditional optical computing systems in terms of integration, multiplexing dimensions, and functional parallelism. As a result, they have become the preferred platform for optical analog computing, advancing novel approaches in optical information sensing and processing.

This paper presents a comprehensive analysis of metasurface-based optical analog computing development, examining three distinct architectural paradigms as illustrated in Figure 1: spectral filtering, Green’s function, and optical pupil function methods. The spectral filtering approach utilizes metasurfaces encoded with coherent transfer functions (CTFs) to substitute filters in 4f systems, enabling precise spatial spectral modulation of input light. This method leverages metasurfaces’ inherent advantages, including flexible structural design and multi-dimensional control capabilities. Various metasurface-based architectures utilizing amplitude, phase, polarization, and multi-dimensional modulations have been developed extensively. These systems demonstrate high computational precision, performing first- to higher-order differentiations and complex convolution operations, while supporting polarization, wavelength, and spatial multiplexing for multi-channel parallel processing. However, this approach requires coherent light sources and maintains a substantial system size. The Green’s function method executes differential operations in real space through metasurfaces with engineered transmittances, offering advantages in compactness and nonlocality. This method typically operates only at resonant wavelength and is restricted to differential operations, with accuracy diminishing in higher-order differentiation. The pupil function method performs operations on the image plane by modulating metalens phase and amplitude associated with different convolution kernels. This approach accommodates broadband light illumination with minimal polarization dependence. It simultaneously achieves optical computing and imaging functions without requiring additional imaging systems. However, multi-dimensional multiplexing demands high metalens processing accuracy, complicating large-scale device fabrication. Additionally, small aperture limits both field of view and imaging resolution. Table 1 presents a comparison of key performances across these three methods.

Metasurface-based optical analog computing provides significant advantages in multi-dimensional processing capabilities, system integration, compactness, and functional parallelism. However, the prevalent electron beam lithography technology used in metasurface preparation faces limitations in processing efficiency and technical complexity. These constraints impede device development and application advancement. Large-scale manufacturing methods such as nanoimprinting and laser direct writing might address these challenges in producing metasurfaces with simpler functions.

Incoherent light represents a fundamental condition for real environment imaging and shows reduced susceptibility to external disturbances. However, its application is limited by the convolution operation kernel’s intrinsic intensity properties, which prevent phase design implementation and direct differential operations. These computational challenges can be addressed through multiple parallel front-end convolution operations enabled by multi-dimensional parameter multiplexing combined with digital processing. Utilizing light field advantages such as broad bandwidth and mode, along with optical neural network architecture, makes optical computing frontends suitable for incoherent light illumination. This approach offers advantages in delay and energy efficiency, establishing foundations for optical processors in autonomous driving and intelligent decision-making applications.

The development of all-optical neural networks using metasurfaces to replace conventional camera systems and subsequent electronic neural networks for direct scene processing shows considerable potential. However, current metasurface-based optical diffraction processors demonstrate limited functionality, while optical neural networks exhibit suboptimal model complexity and experimental performance compared to electronic counterparts. A promising research direction involves integrating optical analog computing as a preprocessing complement to electronic computing, potentially achieving enhanced computational performance. Metasurface-based optical analog computing has demonstrated significant progress, with research indicating substantial potential for practical implementation.