In the past decade, photonics and optoelectronics have significantly progressed in developing new nanofabrication techniques for optical metamaterials. However, the optimal design of such artificial nanostructures remains complex and resource-intensive, often relying on intuition-based models. In this context, machine learning-assisted optimization techniques emerged as a promising approach to achieving high-performance and practical solutions. We discuss diverse inverse design approaches that use machine learning algorithms to optimize the design of nanophotonic metadevices, including the high-efficiency coupling of single-photon sources with photonic waveguides, variable-index multilayer films, active nanophotonic devices and systems, other photonic metastructures with optimized complex topologies.
Sizing of individual particles with nanoscale precision is crucial for the understanding of their physical and chemical properties and for their use in nanodevices. Optical characterization methods are rapid, non-invasive and may provide a wide range of useful information. However, the weak optical response of subwavelength wide-gap dielectric nanoparticles poses a fundamental challenge for their optical metrology. We demonstrate scalable optical sizing of nanodiamonds based on confocal scanning microscopy. The method is absolutely calibrated by an atomic force microscope and paves the way for the facile metrology of a wide range of weakly scattering nano-objects for applications in biomedicine, catalysis, nanotechnology, and quantum optics.
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