Lightweight, miniaturized optical imaging systems are vastly anticipated in these fields of aerospace exploration, industrial vision, consumer electronics, and medical imaging. However, conventional optical techniques are intricate to downscale as refractive lenses mostly rely on phase accumulation. Metalens, composed of subwavelength nanostructures that locally control light waves, offers a disruptive path for small-scale imaging systems. Recent advances in the design and nanofabrication of dielectric metalenses have led to some high-performance practical optical systems. This review outlines the exciting developments in the aforementioned area whilst highlighting the challenges of using dielectric metalenses to replace conventional optics in miniature optical systems. After a brief introduction to the fundamental physics of dielectric metalenses, the progress and challenges in terms of the typical performances are introduced. The supplementary discussion on the common challenges hindering further development is also presented, including the limitations of the conventional design methods, difficulties in scaling up, and device integration. Furthermore, the potential approaches to address the existing challenges are also deliberated.

Replacing electrons with photons is a compelling route toward high-speed, massively parallel, and low-power artificial intelligence computing. Recently, diffractive networks composed of phase surfaces were trained to perform machine learning tasks through linear optical transformations. However, the existing architectures often comprise bulky components and, most critically, they cannot mimic the human brain for multitasking. Here, we demonstrate a multi-skilled diffractive neural network based on a metasurface device, which can perform on-chip multi-channel sensing and multitasking in the visible. The polarization multiplexing scheme of the subwavelength nanostructures is applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable items. The areal density of the artificial neurons can reach up to 6.25 × 10⁶ mm⁻² multiplied by the number of channels. The metasurface is integrated with the mature complementary metal-oxide semiconductor imaging sensor, providing a chip-scale architecture to process information directly at physical layers for energy-efficient and ultra-fast image processing in machine vision, autonomous driving, and precision medicine.

The past few decades have witnessed growing research interest in developing powerful nanofabrication technologies for three-dimensional (3D) structures and devices to achieve nano-scale and nano-precision manufacturing.

Metasurfaces enable the design of optical elements by engineering the wavefront of light at the subwavelength scale. Due to their ultrathin and compact characteristics, metasurfaces possess great potential to integrate multiple functions in optoelectronic systems for optical device miniaturisation. However, current research based on multiplexing in the 2D plane has not fully utilised the capabilities of metasurfaces for multi-tasking applications. Here, we demonstrate a 3D-integrated metasurface device by stacking a hologram metasurface on a monolithic Fabry–Pérot cavity-based colour filter microarray to simultaneously achieve low-crosstalk, polarisation-independent, high-efficiency, full-colour holography, and microprint. The dual functions of the device outline a novel scheme for data recording, security encryption, colour displays, and information processing. Our 3D integration concept can be extended to achieve multi-tasking flat optical systems by including a variety of functional metasurface layers, such as polarizers, metalenses, and others.