Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
The communication module is crucial for control systems. Under thermal, electrical, and mechanical stresses, sensitive digital and analog components may degrade in performance or fail, compromising the module’s long-term stability. Existing reliability-prediction methods, however, do not fully lever...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/8/4356 |
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| Summary: | The communication module is crucial for control systems. Under thermal, electrical, and mechanical stresses, sensitive digital and analog components may degrade in performance or fail, compromising the module’s long-term stability. Existing reliability-prediction methods, however, do not fully leverage multi-physics simulations to model stress-induced failure modes, leading to limited confidence in their predictions. This article proposes a systematic method to enhance the reliability prediction of mixed-signal electronic systems under complex operating conditions. First, we identify the key Complementary Metal-Oxide-Semiconductor (CMOS) chips and their associated failure mechanisms. Then, we use an I/O Buffer Information Specification-based (IBIS-based) topology to build a hybrid-precision simulation that models electrical stress. A Verilog-SPICE framework is employed to simulate component degradation and failure modes. Subsequently, by including mission profiles, we perform a simplified multi-physics coupling analysis to evaluate the effects of temperature and mechanical stress on failure mechanisms. Finally, the Physics of Failure models of components are integrated to derive the reliability curve of the module, and targeted optimization strategies are proposed. Compared to conventional methods, the method combines hybrid-precision simulation with multi-physics coupling modeling, improving the accuracy of critical failure modes. This method enhances the quantitative product reliability analysis and provides valuable support for optimized design. |
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| ISSN: | 2076-3417 |