Machine learning techniques using artificial neural networks (ANN) have proven to be extremely ef-fective in designing nanophotonic systems. This presentation focuses on two applications where ANNs are utilized for designing nanophotonic scatterers.
In the first scenario, ANNs act as surrogate solvers for Maxwell's equations, allowing the design of scatterers tailored to specific fabrication technologies like laser nanoprinting. Designing low-index material scatterers is complex, so solving the inverse problem multiple times from different starting points is crucial. A Fourier neural operator ANN serves as a surrogate Maxwell solver, simplifying this process.
The second scenario integrates ANNs into a holistic metasurface design framework. Individual meta-atoms are efficiently described by their scattering responses, typically expressed as polarizability or T-matrix that provide metasurfaces with functionality on demand. Then, suitably trained ANNs are used to identify feasible physical objects that offer the desired T-matrices.
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