Machine Learning Enhanced Optical Microscopy for the Rapid Morphology Characterization of Silver Nanoparticles.

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.

Physics-Guided Neural-Network-Based Inverse Design of a Photonic-Plasmonic Nanodevice for Superfocusing.

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 physics-guided 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.