A High-Performance Transfer Learning-Based Model for Microwave Structure Behavior Prediction

Abstract

Microwave structure behavior prediction enables the estimation of circuit response over a frequency range, playing a crucial role in the design of radio frequency (RF) structures. Deep neural network (DNN) approaches have demonstrated their capability to simulate microwave structure behaviors. Nonetheless, the quality and utility of the model are constrained by the availability of data and computational capabilities. These inherent disadvantages hinder the extensive application of DNN in microwave structure behavior prediction. Transfer learning has recently been produced as a method offering improved accuracy and speed for predicting microwave circuit behavior. This paper proposes a novel transfer learning-based model to expedite the prediction process for a sequence of frequency samples. Through experimental validation, it is illustrated that the proposed methodology outperforms the conventional DNN techniques for microwave structure behavior prediction by effectively reducing the required data and shortening the training time. The proposed model also facilitates the fine-tuning of hyperparameters and reduces the simulator computing load.

Federated Learning for Microwave Filter Behavior Prediction

“…Training a well-performed DL model for microwave device behavior prediction necessitates a large quantity of highquality training data. However, generating such training data through EM simulation and fine-tuning is a time-consuming and computationally intensive process that demands specialized knowledge and experience in microwave technology [7].…”