TR2019-027

Nanostructured Photonic Power Splitter Design via Convolutional Neural Networks



We train a convolutional neural network (CNN) that can predict the optical response of randomly generated nanopatterned photonic power splitters in a 2 to the 400th power design space with a prediction correlation coefficient of 85%.