Accuracy of CMOS-Based Piezoresistive Stress Sensor for Engineering Applications of Thermal Loading Condition: Theoretical Review and Experimental Validation

Prisacaru, Alexandru; Palczynska, Alicja; Gromala, Przemyslaw Jakub; Wu, Bulong; Han, Bongtae; Zhang, Guoqi

doi:10.1109/jsen.2019.2921406

Cited by 12 publications

(2 citation statements)

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“…One of the most important aspects in PHM is the sensor data selection, as it has to be sensitive to a damage of interest. A piezoresistive silicon-based stress sensor was employed in this study, due to its direct connection to any physical changes in the package [15]. The stresses are calculated based on the two measured quantities: (1) the stresses determined from pMOS and nMOS current values and (2) the temperature determined from the voltages.…”

Section: Methodsmentioning

confidence: 99%

“…This data is then passed through a preprocessing step that removes outliers, smoothens the data and extracts the relative stresses caused by the temperature change. More details about the theoretical background of the stress sensor can be found in [16] and the fundamentals of how to use such sensor in reliability are described in detail in [15]. An acquisition unit (AU) is utilized to acquire the sensor signals, and the second unit is to process and transmit the data (PHM Central Unit).…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%