Condition Monitoring Algorithm for Piezoresistive Silicon-Based Stress Sensor Data Obtained from Electronic Control Units

Prisacaru, Alexandru; Palczynska, Alicja; Gromala, Przemyslaw Jakub; Han, Bongtae; Zhang, Guo Qi

doi:10.1109/ectc.2017.73

Cited by 8 publications

(1 citation statement)

References 16 publications

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“…One of the ways to perform prognostics is to measure the mechanical stresses directly using a stress sensor. Such sensor was developed originally to measure the manufacturing stresses [4], but was extended successfully to various applications including transfer molding [5], packaging [6], molding relaxation [7], prognostics [8] [9] and condition monitoring [10] [11]. In [12] the sensor was packages in a QFN and the stress field has been recorded after the damage.…”

Section: Introductionmentioning

confidence: 99%

Degradation Estimation and Prediction of Electronic Packages Using Data-Driven Approach

Prisacaru

Gromala

Han

et al. 2022

IEEE Trans. Ind. Electron.

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Recent trends in automotive electronics such as automated driving will increase the number and complexity of electronics used in safety relevant applications. Applications in logistics or ridesharing will require a specific year of service rather than the conventional mileage usage. Reliable operations of the electronic systems must be assured at all times, regardless of the usage condition. A more dynamic and on-demand way of assuring the system availability will have to be developed. This paper proposes a thermo-mechanical stress-based prognostics method as a potential solution. The goal is achieved by several novel advancements. On the experimental front, a key microelectronics package is developed to directly apply the prognostics and health management (PHM) concept using a piezoresistive silicon-based stress sensor. Additional hardware for safe and secure data transmission and data-processing is also developed, which is critically required for recording in situ and real-time data. On the data-management front, proper data-driven approaches have to be identified to handle the unique data set from the stress sensor employed in the study. The approaches effectively handle the massive amount of data that reveals the important information and automation of the prognostic process and thus to be able to detect, classify, locate and predict the failure. The statistical techniques for diagnostics and the machine learning (ML) algorithms for health assessment and prognostics are also determined to implement the approaches in a simple, fast but accurate way within the capacity of limited computing power. The proposed prognostics approach is implemented with actual microelectronics packages subjected to harsh accelerated testing conditions. The results corroborate the validity of the proposed prognostics approach.

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