Physical simulation design and optimization of micro structure semiconductor neutron detector

BackgroundMicro structured neutron detectors (MSNDs) are a promising alternative to 3He detectors, offering a compact size, high thermal neutron detection efficiency, low γ sensitivity, low power consumption, and ease of integration into detection systems.PurposeThis study aims to determine the optimal micro-structural parameters and theoretically predict the detection properties through the physical simulation design of MSNDs.MethodsFirstly, the Monte Carlo simulation tool Geant4 was employed to model the MSND based on a high-resistance silicon wafer with a single-sided trench structure, and combined with Stopping and Range of Ions in Matter (SRIM) code to simulate the impact of different 6LiF filling densities on the mean free path of thermal neutrons with full consideration given to the actual 6LiF filling density. Then, the detection efficiency for thermal neutrons and the n/γ discrimination ratio were evaluated as key performance metrics. Finally, the structural parameters were systematically optimized to maximize the detector performance.ResultsThe results indicate that the MSND achieves high thermal neutron detection efficiency and a high n/γ ratio even at a low 6LiF filling density of 1.27 g·cm-3. The optimal structural parameters are determined to be a trench width of 16 μm and a trench distance of 14 μm. While a deeper trench enhances detection efficiency, a depth of 480 μm is selected as the optimal compromise, balancing performance with the mechanical strength of the wafer and the limitations of the etching process. By applying the optimized structure and an energy threshold of 400 keV, the thermal neutron detection efficiency reaches 31.3%, and the n/γ discrimination ratio for 662 keV gamma rays from 137Cs reaches 7.6×103.ConclusionsSimulation results of this study demonstrate that the optimized MSND design significantly outperforms traditional planar semiconductor neutron detectors. The findings provide a valuable reference for the further development of high-performance MSNDs.