微谐振器的机器学习辅助逆设计
Machine Learning Assisted Inverse Design of Microresonators
摘要：
对制造具有所需光学特性的微谐振器的高要求导致了各种优化几何形状、模式结构、非线性和色散的技术。根据应用，在这种谐振器中的色散抵消其光学非线性，并影响腔内光学动力学。在本文中，我们演示了使用一个机器学习（ML）算法作为一种工具，以确定微谐振器的几何形状，从他们的色散曲线。通过有限元模拟生成了具有约460个样本的训练数据集，并使用集成氮化硅微谐振器对模型进行了实验验证。两个ML算法进行了比较，以及适当的超参数调整，其中随机森林（RF）产生最好的结果。模拟数据的平均误差远低于15%。
英文摘要：
The high demand for fabricating microresonators with desired optical properties has led to various techniques to optimize geometries, mode structures, nonlinearities and dispersion. Depending on applications, the dispersion in such resonators counters their optical nonlinearities and influences the intracavity optical dynamics. In this paper, we demonstrate the use of a machine learning (ML) algorithm as a tool to determine the geometry of microresonators from their dispersion profiles. The training dataset with ~460 samples is generated by finite element simulations and the model is experimentally verified using integrated silicon nitride microresonators. Two ML algorithms are compared along with suitable hyperparameter tuning, out of which Random Forest (RF) yields the best results. The average error on the simulated data is well below 15%.
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