Shilong Zhu

To improve the throughput of electron-beam cross section measurement on existing testbeds, we propose a “ $5\times 5$ coarse sampling $+ 15\times 15$ machine-learning reconstruction” scheme. Paired $5\times 5$ / $15\times 15$ current-distribution matrices generated by the MTSS simulation software are used for training and validation. Methodologically, we integrate interpolation alignment and residual correction into a lightweight pipeline: interpolation first performs scale alignment and spatial interpolation to provide a stable prior; supervised learning then pixelwise corrects the systematic interpolation bias, yielding reliable reconstructions from few samples. Featuring low complexity and latency, this pipeline easily integrates into legacy platforms, enabling rapid $5\times 5$ – $15\times 15$ reconstruction on commodity hardware. Compared with the simulated $15\times 15$ Ground Truth, the residual model achieves pixel-level errors of mean absolute error (MAE) $= 1.287\times 10^{-4}$ and root mean squared error (RMSE) $= 1.804\times 10^{-4}$ . In engineering metrics, the beam-waist radius (r $99\cdot 5$ ) errors on the validation set are MAE $\approx ~16.86~\mu $ m and RMSE $\approx ~20.83~\mu $ m. Diagonal line-profile comparisons of the current maps further show samplewise MAE/RMSE at the $10^{-4}$ level, indicating controllable deviations that meet efficiency-improvement requirements and demonstrate practical feasibility for extension to experimental measurements.

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.

This article introduces an innovative design for a thermionic grid-controlled RF electron gun that utilizes a spoof localized surface plasmons (SLSPs) structure as the grid. The strong resonance and high size compression capabilities of SLSP enable the electron gun to achieve reduced current, compact dimensions, and minimal modulation power requirements. The gun emits pre-modulated pulse electron beams (EBs) with a 40GHz repetition rate and an 18 mA peak current. Simulation experiments demonstrate the gun’s capability to detect the relative phase of electrons in the beam-wave interaction (BWI) process using this pre-modulated beam. This novel RF electron gun design opens new avenues for future detection of TWT components.

This paper presents a system for measurement the distribution of complex permittivity (ε) and attenuation of support rods employed in helix travelling-wave tubes (HTWTs). By leveraging the spoof localized surface plasmon (SLSP) structure as a miniaturized resonator, a compression size of λ0/19 (where λ0 represents the free-space wavelength) is attained, featuring six resonant modes within the bandwidth, each with a quality factor (Q-factor) exceeding 80. Grounded on the design of the resonator, a low-cost planar microwave sensor is developed. Through the simulation-driven calibration method, the dielectric constant (ε_r'), loss tangent (tanδ), and attenuation coefficient (A_loss) at various positions of the support rod are obtained by measuring and converting the S-parameters. A flexible hardware system is currently being established, incorporating replaceable fixtures, transmission devices, and a vector network analyzer (VNA). Additionally, a control software is under development to facilitate the control of the measurement system and data processing. The measurement system is utilized for measuring the characteristics of the S-band HTWT support rod within an 80 mm range.

This article presents a study on the utilization of annular beams in broadband helix traveling wave tubes (HTWTs). The research delves into the beam-wave interaction (BWI) performance of HTWTs under different hollow electron beam conditions. The theoretical analysis includes the use of gain parameter $C$ and space charge parameter $QC$ to evaluate the performance within the linear interaction region. Based on the analysis, a 4–20 GHz ultra-wideband traveling wave tube (TWT) based on a hollow beam scheme was designed using the software microwave tube simulator suite (MTSS). The design process involved optimizing the slow wave structure (SWS), the BWI performance, the electron gun, and the magnetic system. An output power greater than 109 W is achieved, and the small signal gain is better than 23 dB across the entire operating bandwidth. The particle-in-cell (PIC) solver in CST was used to verify the saturated gain of the designed TWT.

A microwave metalens, consisting of two circular metasurfaces, is proposed to achieve zoom functionality through mechanical rotation. The phase distribution across different regions on each metasurface follows an arithmetic sequence along the angular direction. Upon rotating one of the metasurfaces, the overall phase distribution on the metalens changes accordingly to meet the requirements of different focal lengths. The metalens operates at 15 GHz with a radius of 80 mm. At rotation angles of 0°, 45°, and 90°, the expected focal lengths are 20 mm, 60 mm, and 100 mm, respectively. The simulated electric field intensity at these focal points is 4.8 V/m, 5.6 V/m, and 4.0 V/m, respectively. Experimental results align closely with simulation outcomes. Due to its straightforward implementation principle, the proposed method holds promise for application in near-field focusing scenarios.