Designing integrated photonic devices using artificial neural networks.

We develop and experimentally validate a practical artificial neural network (ANN) design framework for devices that can be used as building blocks in integrated photonic circuits. As case studies, we train ANNs to model both strip waveguides and chirped Bragg gratings using a small number of simple input and output parameters relevant to designers of integrated photonic circuits. Once trained, the ANNs decrease the computational cost relative to traditional design methodologies by more than 4 orders of magnitude. To illustrate the power of our new design paradigm, we develop and demonstrate both forward and inverse design tools enabled by the ANN. We use these tools to design and fabricate several integrated Bragg grating devices within a useful photonic circuit. The ANN's predictions match the experimental measurements well and do not require any post-fabrication training adjustments.