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.

中文翻译：

---

通过机器学习自动校准基于硅环的光开关

在大规模的基于环的光交换结构中，强烈要求自动校准基于环的光交换器。在机器学习算法的支持下，我们建立了一个人工神经网络（ANN）模型，以便首次从测量的光谱中检索2×2双环辅助Mach-Zehnder干涉仪（DR-MZI）开关的参数。校准算法已在多个设备上验证。可以将光学开关的工作波长调谐到自由光谱范围内的任何波长，精度要高于90 pm。在交叉和棒状状态下，消光比超过20 dB，校准周期不超过7个。自动校准和手动调整之间的电压差小于30 mV，显示了校准算法的高精度。