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.

中文翻译：

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通过深度神经网络预测微谐振器中的克尔孤子梳

克尔孤子梳的形成是一个被广泛认可的重要但复杂的问题，它涉及腔内非线性、色散、模式相互作用和泵浦效应之间的交叉影响。在这里，我们提出并展示了一个深度神经网络模型，通过训练它们的透射光谱来统计地预测二氧化硅微球中的克尔梳状光谱。这种方案能够在特定的泵扫描下识别孤子梳，误差 <8%，经实验测量验证。这项研究将深度学习和微梳光子学联系起来，可以为光子器件的测试和研究提供一个强大而方便的工具。