Automatic calibration of silicon ring-based optical switch powered by machine learning

Abstract

Calibrating ring-based optical switches automatically is strongly demanded in large-scale ring-based optical switch fabrics. Supported by a machine-learning algorithm, we build an artificial neural network (ANN) model to retrieve the parameters of a 2×2 dual-ring assisted Mach-Zehnder interferometer (DR-MZI) switch from the measured spectra for the first time. The calibration algorithm is verified on several devices. The operating wavelength of the optical switch can be tuned to any wavelength in a free spectral range with an accuracy better than 90 pm. The extinction ratio exceeds 20 dB at the cross- and bar-states with no more than 7 calibration cycles. The voltage difference between the automatic calibration and manual tuning is less than 30 mV, showing the high accuracy of the calibration algorithm. Our scheme provides a new way to calibrate ring-based devices that work as optical switch fabrics and tunable optical filters.