This paper reports the development of a 3-D finite-element design tool for the design of permanent-magnet focusing system called the magnetic focusing simulator (MFS). It is a new module of microwave-tube simulator suite developed by the University of Electronic Science and Technology of China. MFS specifically targets problem classes including periodic permanent magnet, wiggler, permanent-magnet quadrupoles, periodic cusped magnet, and so on. MFS is a finite-element code of magnetic scalar potential and is a solver of magnetostatic field. It can be used for designing, optimizing, testing, and validating the permanent-magnetic focusing systems. The theory of MFS is discussed in detail in this paper. Besides, the accuracy is compared with the commercial software ANSYS Maxwell, and the performance of MFS is also shown.

At present, electron optical simulator (EOS) takes a long time to solve linear FEM systems. The algebraic multigrid preconditioned conjugate gradient (AMGPCG) method can improve the efficiency of solving systems. This paper is focused on the implementation of the AMGPCG method in EOS. The aggregation-based scheme, which uses two passes of a pairwise matching algorithm and the K-cyle scheme, is adopted in the aggregation-based algebraic multigrid method. Numerical experiments show the advantages and disadvantages of the AMG algorithm in peak memory and solving efficiency. The AMGPCG is more efficient than the iterative methods used in the past and only needs one coarsening when EOS computes the particle motion trajectory.

The electron optics simulator (EOS) and the magnetic focusing simulator (MFS), part of the microwave tube simulator suite (MTSS), use the finite element method (FEM) to calculate electrostatic fields, electron trajectories, and magnetic fields in both 2-D and 3-D models. With the growing demand for high-precision and multiscale computations, EOS and MFS are required to handle problems with numerous meshes, leading to a sharp increase in the number of unknowns in the matrix equation. To tackle this challenge, EOS and MFS have implemented the domain decomposition method (DDM) based on the dual-primal finite element tearing and interconnecting (FETI-DP) algorithm, which is well-suited for parallel computation and is expected to enhance solving efficiency for large-scale analysis. This article presents the implementation and the computational performance of the FETI-DP algorithm in EOS and MFS. Test results show that the FETI-DP algorithm offers significant advantages in computational time in most cases. Additionally, tests demonstrate that EOS and MFS can perform parallel computing, enabling the analysis of large-scale 2-D axisymmetric electron optics systems and significantly accelerating computation speed.

Recent developments on the electron optics simulator (EOS) are reported in this paper. The EOS is a finite element two-dimensional (2D) and three-dimensional (3D) electrostatic particle steady-state trajectory code that has been widely used for beam optics modeling of vacuum electronic devices. To extend the application scope and improve the computation efficiency, four significant advances have been made and are presented in this paper. First, the code is reprogrammed to consider physical models that contain dielectrics and to support charge excitation. Second, more primary particle emission models are added in EOS to meet the needs for simulating electron and ion optics devices for diverse applications. For the precise simulation of the secondary electron emission, the Vaughan model is improved, and the Furman model is newly added in EOS. Finally, the algebraic multigrid algorithm has been embedded in EOS, which can significantly improve the computation efficiency for problems with large-scale matrices. For these developments, the necessary principles and corresponding performance are shown in detail in this paper.

This study presents an innovative structural design for pixelated electron guns, coupled with a dedicated structural screening algorithm. By constructing a convolutional neural network (CNN) model for the anode component, we achieve high-precision forward prediction of key electron gun parameters—emission current, operational range, and beam waist radius. Simulation-based validation demonstrates a minimal average relative error (ARE) of 0.54% between predicted and simulated results. Furthermore, leveraging the established CNN framework with parameter standardization methodology, we demonstrate rapid inverse synthesis of pixelated electron guns. The inverse design yields an ARE of 8.27% compared to the target specifications. This integrated approach significantly broadens design flexibility while enhancing developmental efficiency for electron gun systems.

In the area of particle emission, new models for thermionic beam representation are included that support primary emission and secondary emission in EOS. Also discussed are new methods for temperature-limited and space-charge-limited emission. A new secondary emission model is presented that captures true secondaries and back-scattered primary electrons.