We present an open-source deep artificial neural network (ANN) model for the accelerated design of polarization-insensitive subwavelength grating (SWG) couplers on the silicon-on-insulator platform. Our model can optimize SWG-based grating couplers for a single fundamental-order polarization, or both, by splitting them counter-directionally at the grating level. Alternating SWG sections are adopted to reduce the reflections (loss) of standard, single-etch devices-further accelerating the design time by eliminating the need to process a second etch. The model of this device is trained by a dense uniform dataset of finite-difference time-domain (FDTD) optical simulations. Our approach requires the FDTD simulations to be made up front, where the resulting ANN model is made openly available for the rapid, software-free design of future standard photonic devices, which may require slightly different design parameters (e.g., fiber angle, center wavelength, polarization) for their specific application. By transforming the nonlinear input-output relationship of the device into a matrix of learned weights, a set of simple linear algebraic and nonlinear activation calculations can be made to predict the device outputs 1,830 times faster than numerical simulations, within 93.2% accuracy of the simulations.

artificial neural networks, grating couplers, machine learning, polarization insensitivity, Silicon photonics, subwavelength devices
dx.doi.org/10.1109/JSTQE.2018.2885486
IEEE Journal of Selected Topics in Quantum Electronics
Department of Electronics

Gostimirovic, D. (Dusan), & Ye, W.N. (2018). An Open-Source Artificial Neural Network Model for Polarization-Insensitive Silicon-on-Insulator Subwavelength Grating Couplers. IEEE Journal of Selected Topics in Quantum Electronics. doi:10.1109/JSTQE.2018.2885486