Nanophotonics is advancing technological innovations across various fields, necessitating intricate photonic microstructures for nanoscale light manipulation. Designing these microstructures is complex and often evades conventional optimization methods. Optical metasurfaces offer a solution by miniaturizing optical components onto a single plane. Their functionality stems primarily from their structure and orientation rather than the materials themselves. These surfaces, built from sub-wavelength structures, precisely control light’s phase, polarization, mode, and spectrum, enabling the creation of custom light patterns for integrated photonics applications such as light coupling, gratings, and lenses.
The design of a metasurface hinges on the size, orientation, shape, and arrangement of its meta-atoms, the individual scattering units. While analytical and numerical methods exist to predict metasurface electromagnetic behavior, they are often slow and limited when meta-atom dimensions approach the light’s wavelength. Numerical simulations like FEM, FDTD, and FIT, though used, rely on trial-and-error, hindering the design of complex devices.
To address these limitations, researchers are exploring machine-learning approaches, particularly Deep Neural Networks (DNNs). DNNs offer faster and more effective routes to design metasurfaces. These algorithms can model intricate, non-linear relationships, creating nanophotonic simulators that bypass time-consuming conventional methods. This includes developing both ‘forward models’ that predict electromagnetic responses from given designs, and ‘inverse models’ that determine design parameters for desired responses.
One specific application being explored is photonic beam engineering using meta-scatterer distributions in planar waveguides. The ability to create tailored beam profiles like Gaussian, focused, and collimated beams enhances the efficiency of integrated opto-fluidic sensors used for analyte excitation in on-chip fluorescence and IR spectroscopy. This integrated approach offers advantages including scalability for large-scale field of view by using many excitation sources, compact and cost-effective hardware, and automation of biological analyses. Furthermore, it can lead to improved grating couplers for photonic integrated circuits, maximizing power transmission through fiber couplers. This technology also paves the way for edge emitter laser sources to be used in vertical light excitation applications, relevant to LiDAR and 3D depth sensing.
Recent studies highlight the increasing interest in using DNNs for metasurface design. Research has demonstrated the effectiveness of Artificial Neural Networks for RF and microwave designs, Multi-layer perceptron models for studying photonic crystal dispersion, and hybrid EM optimization methods enhanced with machine learning for predicting metamaterial permittivity. Generative Neural Networks and Generative Adversarial Networks are also being applied to efficiently explore design spaces and optimize metasurface topology, showing the broad adoption of machine learning in this field to create advanced nanophotonic devices.
In a specific design, a planar metasurface with a 5×5 array of meta-scatterers is being used to diffract engineered light beams from a photonic waveguide made of silicon nitride (SiN) on a silicon dioxide-on-silicon wafer. This design focuses on controlling light diffraction through key parameters: periodicity of the scatterers or grating period, the gap between scatterers described by the gap factor, the height or etch depth of the scatterers, and the size of individual scatterers, particularly their width in the direction perpendicular to light propagation. These parameters influence the diffraction angle and energy leakage of the light beam, ultimately shaping the output beam profile. The relationship between these design parameters and the resulting light diffraction is being exploited to develop machine learning models capable of solving the inverse problem: determining the necessary metasurface structure to achieve a desired light diffraction pattern.
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