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.
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