# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 1 Introduction
[PERSON] [11] applied the Arrhenius and Coffin-Manson models to predict inverter reliability under thermal cycling and compared the results with MIL-HDBK-217 and RDF 2000 handbooks, demonstrating improved prediction accuracy. [PERSON] [12] proposed a stochastic reliability prediction model for electronic systems based on the Markov process and corresponding reliability improvement strategies. [PERSON] [13] integrated PoF and copula Bayesian networks to develop a reliability assessment method for complex electronic systems, addressing challenges associated with coupled failure mechanisms. However, the methods mentioned above directly consider the combined effects of multiple stress fields (such as electrical stress, thermal stress, and mechanical strain) on components. In summary, traditional reliability-prediction methods have made significant progress in the field of electronic systems but continue to exhibit notable limitations, particularly in their ability to accurately reflect the complex characteristics of circuit ports. For instance, existing simulation methods for digital-analog mixed circuits, such as VHDL-AMS and UVM, still encounter challenges related to high computational complexity and insufficient accuracy. Additionally, conventional reliability modeling techniques, including ReliabilityBlock Diagrams (RBDs) and Fault Tree Analysis (FTA), rely heavily on statistical data and struggle to effectively capture the coupling effects of electro-thermal-mechanical multi-stress fields on the combined impact on components. Furthermore, the internal structure of integrated devices is inherently complex, and current reliability-prediction approaches based on Physics of Failure (PoF) rarely identify specific failure mechanisms, such as Negative Bias Temperature Instability (NBTI) effects or solder joint fatigue. This limitation results in the inadequate integration of failure mode analysis and multi-stress field simulations, thereby compromising the accuracy of predictions regarding dynamic stress interactions. [PERSON] et al. [14] utilized continuous-time Markov chains to analyze the reliability of aerospace control systems under cascading failures. Through parameter sensitivity analysis, they selected different failure rates \(\lambda\) and priority orders for various modules to analyze their impact on system reliability, providing guidance for key failure module analysis and resource optimization. Among these, the issues concerning aerospace communication modules are particularly prominent. [PERSON] et al. [15] confirmed that the intense thermal cycles and mechanical vibrations during spacecraft on-orbit operations significantly accelerate the failure of related modules, such as FPGA and SRAM, through mechanisms like plastic strain accumulation in solder joints and enhanced NBTI effects. [PERSON] et al. [16] further pointed out that under high-temperature and high-frequency operations, charge and discharge effects (SESDs) induced by spaceborne charged particles exacerbate errors and failures in FPGAs and SRAMs. [PERSON] et al. [17] proposed a method based on Architecture Analysis and Design Language (AADL) to establish a reliability model that includes aerospace communication module platforms, providing AADL error models for fault propagation among components, software, and hardware. However, these methods do not perform failure physics modeling for dynamic stresses on various critical devices, resulting in significant deviations in lifespan predictions. To address these challenges, this study presents a reliability-prediction framework that integrates a hybrid precision simulation with multi-stress field coupling modeling. The key innovations of this approach are as follows: first, hybrid precision circuit simulation is employed and Verilog-SPICE heterogeneous modeling is used for high-precision analysis in critical paths, while behavioral-level abstraction is applied to non-critical areas, effectively balancing simulation efficiency and accuracy.