  Predicting physical response of an artificially structured material is of
particular interest for scientific and engineering applications. Here we use
deep learning to predict optical response of artificially engineered
nanophotonic devices. In addition to predicting forward approximation of
transmission response for any given topology, this approach allows us to
inversely approximate designs for a targeted optical response. Our Deep Neural
Network (DNN) could design compact (2.6x2.6 {\mu}m2) silicon-on-insulator
(SOI)-based 1 X 2 power splitters with various target splitting ratios in a
fraction of a second. This model is trained to minimize the reflection (smaller
than 20 dB) while achieving maximum transmission efficiency (above 90%) and
target splitting specifications. This approach paves the way for rapid design
of integrated photonic components relying on complex nanostructures.
