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.