Potentials of machine learning in vacuum electronic devices demonstrated by the design of a magnetron injection gun

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Great progress has been made on machine learning and its applications are expanding rapidly nowadays. Through the case study of optimizing a magnetron injection gun for gyrotron devices, the functions of machine learning were investigated by using two supervised learning algorithms, regression trees and artificial neural networks. They showed excellent performance in predicting the outputs, exploring the importance of the input parameters and the relationship with the output parameters. Machine learning can be a useful tool in the development of microwave vacuum electron devices.
Original languageEnglish
Pages (from-to)3028 - 3033
Number of pages6
JournalIEEE Transactions on Electron Devices
Issue number6
Early online date5 May 2021
Publication statusPublished - 1 Jun 2021


  • machine learning
  • microwave vacuum electron device
  • magnetron injection gun
  • regression tree
  • artificial neural networks
  • supervised learning

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