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

Jiteng Ma, Shuping Dang, Gavin T Watkins, Kevin A Morris, Mark A Beach

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)
5 Downloads (Pure)


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 brief 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.
Original languageEnglish
Pages (from-to)4394-4398
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Issue number12
Early online date18 Jul 2023
Publication statusPublished - 1 Dec 2023

Bibliographical note

Funding Information:
This work was supported by the Toshiba's Bristol Research and Innovation Laboratory Contribution to the U.K. Research and Innovation (UKRI)/Engineering and Physical Sciences Research Council (EPSRC) Prosperity Partnership in Secure Wireless Agile Networks (SWAN) under Grant EP/T005572/1.

Publisher Copyright:
© 2004-2012 IEEE.


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