# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 1 Introduction  
Communication modules primarily handle bus control interfaces, facilitate asynchronous transmission and reception across different modules, and manage synchronization signals (SYNC). Ensuring their reliability is crucial for optimal system performance. Reliability predictions are conducted based on operational principles and environmental conditions to support product optimization during the design and manufacturing phases. The reliability modeling of electronic modules presents significant complexity. Digital-analog mixed-signal circuit systems within these modules encompass various digitally integrated chips and interface circuits, making it challenging for conventional circuit simulations to accurately capture timing relationships and signal integrity [1]. Due to the mixed-signal nature of the communication module, its components are sensitive to environmental conditions. The combination of thermal, electrical, and mechanical stresses leads to intricate failure mechanisms, further complicating reliability-prediction modeling [2, 3]. Because of the mixed-signal nature of the communication module, its reliability prediction presents significant challenges. Consequently, simulation techniques for mixed-signal circuits have become essential for improving reliability-prediction accuracy. Current mixed-signal simulation primarily employs a coupling method [4], wherein digital and analog simulators operate concurrently, while a dedicated simulation platform manages their communication and coordination. Compared to SPICE-based analog simulators, digital modeling techniques, such as VHDL-AMS, Verilog-A, and Verilog-AMS, improve efficiency by reducing the simulation time. Recent advancements in real-number modeling (e.g., Verilog-AMS with wreal and SystemVerilog RNM) have further enhanced the simulation speed, making it comparable to purely digital models. [PERSON] [5] implemented real-number models for sigma-delta analog-to-digital converters (ADCs), voltage-controlled oscillators (VCOs), and digital phase-locked loops (PLLs) using SystemVerilog, achieving faster simulations compared to SPICE. Moreover, studies [6] have proposed a mixed-signal verification framework based on the Universal Verification Methodology (UVM), balancing accuracy and efficiency in mixed-signal circuit simulation. Despite these advancements, existing methods still face challenges, such as high computational complexity and limited capability for multi-stress field simulations. In reliability prediction, electronic component failure modes are generally categorized as performance degradation and functional failure. Performance degradation refers to the gradual deviation of component parameters from their designed values over time or environmental exposure, while functional failure manifests as abrupt changes in the circuit topology, such as short circuits or open circuits. The Physics of Failure (PoF) method quantifies failure processes based on fundamental physical and chemical principles, leading to more precise reliability predictions. For instance, [PERSON] [7] investigated the failure mechanisms of thick-film resistors and developed a failure time prediction model based on parameter drift. [PERSON] et al. [8] analyzed Schottky diode failures under discharge conditions, revealing a mechanism involving trap accumulation at the anode metal/GaN interface. [PERSON] [9] examined the gate failure of SiC MOSFETs under a short circuit, identifying critical failure points within the SiO\({}_{2}\) dielectric layer. This article establishes corresponding PoF models for different failure modes as a foundation for reliability prediction. Common methods in electronic system reliability modeling include reliability block diagrams (RBDs), fault tree analysis (FTA), Markov processes, and GO analysis [10]. RBD and FTA are primarily employed for system-failure-rate evaluations, whereas Markov processes and GO analysis are used to assess failure impact.