Efficient Broadband Modeling of Microwave Devices with Machine Learning and Analytical Extension of Eigenvalues

Accurate modeling of the broadband behavior of RF and microwave devices is of great interest as it can facilitate design optimization and high-level simulation. Machine learning (ML) techniques, in particular neural networks (NN), have recently gained recognition as a powerful tool for this purpose. Once trained, NNs can serve as an alternative to conventional methods such as full-wave simulations, which can be computationally expensive, or empirical models, which can have limited ranges of validity and accuracy. On the other hand, traditional NN-based modeling requires a large amount of training data and tends to have drastically worse performance on out-of-domain inputs. This coupled with the high computational cost for obtaining training data, severely hinders the application of NNs in device modeling.