Predicting Kerr Soliton Combs in Microresonators via Deep Neural Networks
Teng Tan, Cheng Peng, Zhongye Yuan, Xu Xie, Hao Liu, Zhenda Xie, Shu‐Wei Huang, Yunjiang Rao, Baicheng Yao
Abstract
Formation of the Kerr soliton combs is a widely recognized important but complex issue, which relates to cross-influences among intra-cavity nonlinearities, chromatic dispersions, mode interactions, and pumping effects. Here, we propose and demonstrate a deep neural network model to predict Kerr comb spectra in silica microspheres statistically, via training their transmission spectra. Such a scheme enables soliton comb identification under a particular pump scanning, with error <; 8%, verified by experimental measurements. This study bridging the deep learning and the microcomb photonics, may provide a powerful and convenient tool for photonic device test and investigation.