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Freeform nanostructures have the potential to support complex resonances and their interactions, which are crucial for achieving desired spectral responses. However, the design optimization of such structures is nontrivial and computationally intensive. Furthermore, the current "black box" design approaches for freeform nanostructures often neglect the underlying physics. Here, a hybrid data‐efficient neural optimizer for resonant nanostructures by combining a reinforcement learning algorithm and Powell's local optimization technique is presented. As a case study, silicon nanostructures with a highly‐saturated red color are designed and experimentally demonstrated. Specifically, color coordinates of (0.677, 0.304) in the International Commission on Illumination (CIE) chromaticity diagram – close to the ideal Schrödinger's red, with polarization independence, high reflectance (>85%), and a large viewing angle (i.e., up to ± 25°) is achieved. The remarkable performance is attributed to underlying generalized multipolar interferences within each nanostructure rather than the collective array effects. Based on that, pixel size down to ≈400 nm, corresponding to a printing resolution of 65000 pixels per inch is demonstrated. Moreover, the proposed design model requires only ≈300 iterations to effectively search a thirteen‐dimensional (13D) design space – an order of magnitude more efficient than the previously reported approaches. The work significantly extends the free‐form optical design toolbox for high‐performance flat‐optical components and metadevices. [ABSTRACT FROM AUTHOR]

The development of high‐efficiency compact non‐mechanical beam tuning devices has attracted a lot of attention for light detection and ranging, augmented reality display, and chip‐to‐chip communication. Owing to the fast wavefront manipulation in an ultra‐thin dimension, metasurfaces have been regarded as potential substitutes for traditional tunable optical components toward further miniaturization and low power consumption. However, most beam tuning metasurfaces currently are polarization‐sensitive and designed to work in reflection mode, which limit their applications in integrated optical systems for full‐range steering. In this paper, a transmission mode polarization‐insensitive beam switching metasurface based on nonvolatile phase‐change material Ge2Sb2Te5 is proposed and experimentally demonstrated at the telecommunication wavelength. The high transmission efficiency with a large switching angle of up to 75° is achievable for potentially full‐range beam steering applications. As a proof of concept, the transmitted beam with a switching angle of 15° and directivity of 82.4% is demonstrated. In addition, by controlling the phase transition in the intermediate states, the metasurface can be used as a tunable beam splitter to control the ratio of the beam power between two predesigned transmission angles. The demonstrated phase‐change metasurfaces pave the way for achieving high‐efficiency dynamic beam steering for various important applications. [ABSTRACT FROM AUTHOR]