Fast Wavelength Seeking in a Silicon Dual-Ring Switch Based on Artificial Neural Networks

Abstract

We propose and experimentally demonstrate an automated wavelength seeking scheme for an O-band 1 × 2 silicon photonic switch based on an add-drop structure with two cascaded resonators. In the scheme, two three-layer neural networks are employed to learn the relationship between the monitored optical powers and the heating voltages of the two coupled ring resonators. Then, the two neural networks can quickly predict the required heating voltages according to the monitored optical powers. Therefore, the wavelength seeking can be realized through a training process, which is carried out only once, and a seeking process. By using this scheme, the dual-ring switch can be locked to the wavelength of the input light with a constant duration of 860 μs. The maximum seeking errors for the two rings are 0.08 and 0.09 nm, respectively.