Soft errors induced by radiation are the major reliability threat for SRAM-based field-programmable gate arrays (FPGAs). A more detailed analysis of the soft error sensitivity of the 40 nm SRAM-based FPGA was performed. Experimental methods for the configurable logic module, configure memory cells, and block RAM have been introduced for measuring the single event effects (SEEs) induced by alpha particles using a ²⁴¹Am radiation source. The single event upset (SEU) and single event functional interrupt (SEFI) cross sections of different functional blocks have been calculated to discuss the failure mechanisms of the FPGA. The SEEs test results for the FPGA device based on the 40 nm CMOS process are significant.

To achieve imaging of objects using cosmic ray muons, it is essential to determine the muon trajectories, and accurately locating the muon hit points is key to trajectory reconstruction. Current mainstream muon track detection systems require multiple electronic channels to precisely pinpoint the muon hit points, resulting in complex structures and high costs. The aim of this study is to design a muon track detection system characterized by simplicity, low cost, and high precision. This system is based on Geant4 software and features a circular plastic scintillator without segmentation coupled with Silicon Photon Multipliers (SiPMs). The simulation involves the use of SiPMs to collect the number of photons and the time of triggering SiPM responses as characteristic parameters, and employs a recurrent neural network (RNN) algorithm as the method for muon localization. The simulation results show that a plastic scintillator with a diameter of 20 cm and a thickness of 10 mm can achieve a position resolution of less than 1 cm while ensuring a large detection area. The more SiPMs are coupled, the more accurate the collected photon information is, which improves the position resolution of the detector. The reconstruction accuracy of the Gated Recurrent Unit (GRU) algorithm is comparable to that of the Long Short-Term Memory (LSTM) algorithm within the uncertainties, but the reconstruction speed of the GRU algorithm is better than that of the LSTM algorithm. Under the same conditions, the training speed of the GRU algorithm is about 9/11 of that of the LSTM algorithm. The results of the multi-layer detection unit optimization scheme proposed in this study indicate that as the minimum number of detection unit layers in the muon positioning module increases, the position resolution of the system gradually improves. Considering the cost, the GRU algorithm can be chosen as the reconstruction algorithm, and the minimum detection unit with 8 SiPMs coupled in two layers can be selected as the system optimization scheme. Considering the accuracy of position resolution, the LSTM algorithm can be chosen as the reconstruction algorithm, and the minimum detection unit with 6 SiPMs coupled in three layers can be selected as the system optimization scheme. The multi-unit integrated muon detection system proposed in this study ensures a large detection area while enabling the simplest minimum detection units in a multilayer structure to work in coordination, thereby optimizing the position resolution of the entire muon detection system.

This work constructed a data acquisition system based on a Field Programmable Gate Array (FPGA) integrated with Analog-to-Digital/Digital-to-Analog (AD/DA) converters and conducted single-event effect (SEE) irradiation experiments using an alpha source to investigate how SEEs affected system performance and to characterize the distribution of output waveform anomalies. The results showed that sine and sawtooth signals exhibited more pronounced phase deviations and amplitude distortions under irradiation, while triangular waves displayed relatively smaller variations. A quantitative analysis revealed the statistical distribution of different fault categories and provided a preliminary understanding of the mechanisms underlying SEE-induced anomalies in the data acquisition system. These findings offered valuable insights for enhancing the radiation hardness of FPGA-based data acquisition systems.