Controlling the nanoscale light−matter interaction using superfocusing hybrid photonic−plasmonic devices has attracted significant research interest in tackling existing challenges, including converting efficiencies, working bandwidths, and manufacturing complexities. With the growth in demand for efficient photonic−plasmonic input−output interfaces to improve plasmonic device performances, sophisticated designs with multiple optimization parameters are required, which comes with an unaffordable computation cost. Machine learning methods can significantly reduce the cost of computations compared to numerical simulations, but the input−output dimension mismatch remains a challenging problem. Here, we introduce a physicsguided two-stage machine learning network that uses the improved coupled-mode theory for optical waveguides to guide the learning module and improve the accuracy of predictive engines to 98.5%. A near-unity coupling efficiency with symmetry-breaking selectivity is predicted by the inverse design. By fabricating photonic−plasmonic couplers using the predicted profiles, we demonstrate that the excitation efficiency of 83% on the radially polarized surface plasmon mode can be achieved, which paves the way for super-resolution optical imaging.
The
rapid characterization of nanoparticles for morphological information
such as size and shape is essential for material synthesis as they
are the determining factors for the optical, mechanical, and chemical
properties and related applications. In this paper, we report a computational
imaging platform to characterize nanoparticle size and morphology
under conventional optical microscopy. We established a machine learning
model based on a series of images acquired by through-focus scanning
optical microscopy (TSOM) on a conventional optical microscope. This
model predicts the size of silver nanocubes with an estimation error
below 5% on individual particles. At the ensemble level, the estimation
error is 1.6% for the averaged size and 0.4 nm for the standard deviation.
The method can also identify the tip morphology of silver nanowires
from the mix of sharp-tip and blunt-tip samples at an accuracy of
82%. Furthermore, we demonstrated online monitoring for the evolution
of the size distribution of nanoparticles during synthesis. This method
can be potentially extended to more complicated nanomaterials such
as anisotropic and dielectric nanoparticles.
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