# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 6 Reliability Prediction of the Communication Module Under the Mission Profile
### Overall Reliability Prediction of the Communication Module  
To predict the overall reliability of the communication module, the reliability of key components, FPGA and SRAM, must be calculated. In the device, every key component's failure can lead to module failure. Since the failure mechanisms in the article are relatively independent, the reliability of each failure can be calculated separately, and then, the reliabilities for all mechanisms are used to determine the final reliability of the entire module. Based on this, the reliability block diagram for the overall reliability prediction of the communication module is shown in Figure 28.  
The formula for predicting the overall reliability of the communication module is as follows:  
\[R=R_{\text{FPGA}}\,\times\,R_{\text{SRAM}}=R_{\text{FPGA-sj}}\,\times\,R_{ \text{FPGA-n-bti}}\,\times\,R_{\text{SRAM-sj}}\,\times\,R_{\text{SRAM-n-bti}} \tag{12}\]  
Figure 28: Overall reliability-prediction process of the communication module. Figure 27: Reliability curve of SRAM solder joints. where \(R\) is the overall reliability of the communication module, \(R_{\text{FPGA-s}}\) is the reliability of the FPGA under the solder joint fatigue mechanism, \(R_{\text{FPGA-hbi}}\) is the reliability of the FPGA under the NBTI effect, \(R_{\text{SRAM-sj}}\) is the reliability of the SRAM under the solder joint fatigue mechanism, and \(R_{\text{SRAM-nbi}}\) is the reliability of the SRAM under the NBTI effect. The overall reliability of the communication module is calculated using Equation (12), and the resulting reliability curve is shown in Figure 29.  
Analyzing the obtained reliability curve, it can be observed that the reliability of the communication module begins to decline at around 11 years, reaching zero at approximately 14.5 years. The time at which the reliability drops to 0.9 is around 12 years. Comparing with historical test data, the model accurately predicts the failure time and reliability variation of the communication module, indicating that the model's accuracy meets the practical requirements. Based on hybrid-precision simulation and multi-physics coupling models, this article predicts the overall lifetime of communication modules under aerospace mission profiles to be 14.5 years (12 years when reliability drops to 0.9). To validate the scientific validity of the method, Table 7 compares the results of this article with existing typical reliability prediction methods, covering key indicators, such as the error range, modeling dimensions, and dynamic feedback capabilities. The prediction error is based on the standard deviation of 10 samples under the same experimental conditions. Dynamic feedback is defined as the multi-stress field coupling modeling capability, where coupling parameters are automatically transferred based on data processing platforms, such as Isight, while static feedback requires manual transfer of coupling parameters between different models. As shown in Table 7, traditional methods, such as MIL-HDBK-217, rely on static stress assumptions, leading to prediction errors as high as \(\pm\)20%. While the Bayesian network method proposed by [PERSON] et al. incorporates multi-stress field coupling, it cannot dynamically capture component-level failure mechanisms, like NBTI or solder joint fatigue. In contrast, the multi-physics coupling framework presented in this article significantly enhances accuracy through key improvements. Figure 29: Reliability curve of the communication module. One such improvement is the modeling of dynamic stress interactions. For example, plastic strain in FPGA solder joints leads to an increase in contact resistance, which in turn causes fluctuations in the power supply voltage. This exacerbates the NBTI effect, increasing the threshold voltage, thus creating a closed-loop failure chain. Another advancement is the integration of component-level failure mechanisms, where the PoF model quantifies threshold voltage drift and solder joint fatigue.