Inverse Design of Electron Gun With Transfer Learning Based on Neural Network

Abstract

This article presents an inverse design with transfer learning based on neural network (ID-TL-NN) for the rapid design of electron guns, which expands the range of the structural parameter designs through TL. This ID-TL-NN method can quickly predict electron beam trajectory envelopes and beam waist radius based on given structural parameters. Moreover, it has inverse design function, which can rapidly design corresponding electron gun structures based on given target electron beam envelopes and beam waist radius. The simulation results show that the beam waist radius error is less than 5% compared with the value of the target radius. Furthermore, through TL, the proposed model can extend the range of the structural parameters of the electron gun, achieving high-precision design with only a small number of samples. The model trained with a limited sample set predicts a beam waist radius error of 5%. Compared with traditional methods, this approach significantly increases the efficiency and accuracy of the electron gun design.