A Universal Deep Learning Model for Predicting Detection Performance and Single-Event Effects of SPAD Devices

Single-event effects (SEEs) present a significant challenge to the radiation reliability of integrated circuits. Conventional SEE analysis methods for single-photon avalanche diode (SPAD) devices primarily rely on Sentaurus Technology Computer-Aided Design (TCAD) numerical simulation, which is computationally intensive and time-consuming. In this study, we propose a generalized deep learning (DL) model, using a silicon-based SPAD device with a double-junction double-buried-layer (DJDB) structure fabricated in 180 nm CMOS process as the research subject. By incorporating key parameters that influence SEEs as model inputs, the proposed approach enables rapid prediction of critical parameter metrics, including transient current peaks and dark count rates. Experimental results show that the DL model achieves a prediction accuracy of 97.32% for transient current peaks and 99.87% for dark count rates, demonstrating extremely high prediction precision. To further validate the generalization capability of the proposed network, the model is applied to predict the detection performance of the DJDB-SPAD device. The prediction accuracies for four key performance parameters all exceed 97.5%, further confirming the accuracy and robustness of the developed model. Meanwhile, compared with the conventional Sentaurus TCAD simulation method, the proposed method achieves a 336-fold improvement in computational efficiency. Overall, this method realizes the dual advantages of high precision and high efficiency, which provides an efficient and accurate technical solution for the rapid characteristic analysis and reliability evaluation of SPAD devices under single-event effects (SEEs).