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SUMMARY
| S. An et_al: Metasurface embedded design undesired transmission/reflection sidebands outside the operating wavelength range prove challenging. In recent years, deep learning approaches and deep neural_networks (DNNs) have been investigated as a solution to handle such complex photonic design problems, including the design of multilayer structures, freeform meta-atoms and diffractive imagers. The authors expand the repertoire of deep-learning-based photonic design by showing that DNNs can also be implemented to both predict and inverse design broadband complex S-parameters of active metasurface elements. To illustrate the utility of the hybrid design approach . . .
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