Engineering

Silicon on insulator

Materials science

Inverse

Metamaterial

Computer science

Silicon

Nanophotonics

Photonics

Physics

Mathematics

Optics

Electronic engineering

Artificial intelligence

Geometry

Splitter

Topology (electrical circuits)

Artificial neural network

Optoelectronics

Electrical engineering

Scientific Reports

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 (2.6 × 2.6 μm 2 ) silicon-on-insulator (SOI)-based 1 × 2 power splitters with various target splitting ratios in a fraction of a second. This model is trained to minimize the reflection (to 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.

Deep Neural Network Inverse Design of Integrated Photonic Power Splitters