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.

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

Wei Gao, Liangjun Lu, Linjie Zhou, and Jianping Chen
Opt. Express 28(7) 10438-10455 (2020)

Deep-Neural-Network-Based Wavelength Selection and Switching in ROADM Systems

Weiyang Mo, Craig L. Gutterman, Yao Li, Shengxiang Zhu, Gil Zussman, and Daniel C. Kilper
J. Opt. Commun. Netw. 10(10) D1-D11 (2018)

Accelerating silicon photonic parameter extraction using artificial neural networks

Alec M. Hammond, Easton Potokar, and Ryan M. Camacho
OSA Continuum 2(6) 1964-1973 (2019)

On-chip spiking neural networks based on add-drop ring microresonators and electrically reconfigurable phase-change material photonic switches

Qiang Zhang, Ning Jiang, Yiqun Zhang, Anran Li, Huanhuan Xiong, Gang Hu, Yongsheng Cao, and Kun Qiu
Photon. Res. 12(4) 755-766 (2024)

Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks

Yihao Xu, Xianzhe Zhang, Yun Fu, and Yongmin Liu
Photon. Res. 9(4) B135-B152 (2021)