TR2018-183

Deep Neural Network Inverse Modeling for Integrated Photonics


We propose a deep neural network model that instantaneously predicts the optical response of nanopatterned silicon photonic power splitter topologies, and inversely approximates compact (2.6 x 2.6 um2) and efficient (above 92%) power splitters for target splitting ratios.

 

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