From non-resonant to resonant meta-devices: imaging, color routing, displaying, and beyond

2026-06-29

<p<The rapid evolution of meta-optics has been largely driven by non-resonant phase-control mechanisms, chiefly geometric and propagation phases, enabling high-efficiency, broadband devices such as achromatic metalenses and structural-color displays. Recently, however, resonant physics, especially bound states in the continuum (BICs) and local to nonlocal transition (LNT), has introduced a fundamentally new paradigm. This review highlights how the shift from non-resonant to resonant meta-devices is reshaping imaging and display technologies. Resonant architectures unlock unprecedented functionalities that were previously inaccessible with conventional phase-only designs, including ultra-narrowband wavefront control, spectrally decoupled multiplexing, and pixel-level color routing with high spectral purity. We systematically compare the underlying physics, illustrate key advances in resonant imaging, color manipulation, and structural-color displays, and discuss emerging applications in augmented/virtual reality (AR/VR) and sensing. Ultimately, we outline how resonant meta-optics is expanding the design landscape, offering new avenues toward dynamic, multifunctional, and wavelength-selective photonic systems.</p<

Opto-Electronic Science. 2026 5(7): 260016. Published 2026-06-29

Scalable spatiotemporal interleaving network for high-density integrated photonic convolution

2026-07-24

<p<Efficient edge intelligence is fundamentally constrained by data movement overhead in conventional electronic hardware, motivating alternative computing paradigms with intrinsic parallelism. Although integrated photonic processors provide multiple parallel dimensions, established architectures remain limited by the quadratic scaling of both footprint and control complexity with rising computational throughput. Here, we propose an on-chip spatiotemporal photonic interleaving network (SPIN) that enables convolutional acceleration by recursively interleaving distributed delay lines into shared physical channels, thereby redistributing throughput scaling from spatial replication to wavelength multiplexing while reducing waveguide footprint to <italic<O</italic<(<italic<K</italic< log<sub<2</sub< <italic<K</italic<) and active control complexity to <italic<O</italic<(<italic<K</italic<). Experimental results demonstrate high-fidelity convolution with a correlation coefficient exceeding 0.98 on the Modified National Institute of Standards and Technology (MNIST) dataset, complemented by programmable multi-task operation and configurable kernel geometry. The SPIN architecture is capable of supporting a projected single-core throughput of 29.7 TOPS upon full exploitation of the available spectrum. These results validate a structurally scalable and energy-efficient photonic computing framework, advancing the viability of integrated optical accelerators for next-generation edge AI.</p<

Opto-Electronic Science. 2026 5(7): 260018. Published 2026-07-24