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Nowadays electronic systems used in automotive industry consolidate a variety of functionalities and features within one unit. Such smart systems consist of standard IC packaging, sensors and actuators. It is required that such systems will survive about 15 years of usage. To assure correct functionality prognostic and health monitoring (PHM) methods can be used. The potential benefits from implementation of these methods include among others reduction of maintenance costs and early warning of possible failure. For automotive industry, for which typical load history is not sufficiently known, this seems to be suitable approach. In today's reliability models some assumptions about the typical load history have to be made, which leads to inaccurate estimations on the remaining useful life. Though, in PHM approach one wants to make a prognosis of a failure so that maintenance can be done at the appropriate time. In this paper, the acquisition unit for in-situ measurements of internal stresses in a novel smart system module is developed. It allows recording the stresses from multiple piezoresistive sensors, at different locations on a surface of a chip, at the same time. As the measurements, that are planned, will be conducted when the smart system is assembled in the engine compartement, it is required that the acquisition unit is portable and immune to harsh conditions. The results of the measurements will be used to monitor the evolution of stress magnitudes in real work conditions

The aim of this paper is to get insight into measurement uncertainties for thermomechanical measurements performed using a piezoresistive silicon-based stress sensor in a standard microelectronic package. All used sensors have the same construction, were produced in the same technological processes at the same time, yet the measurement results show significant distribution. The possible causes for this phenomenon are discussed in this paper. Additionally, Finite Element Method (FEM) model is created and validated, what enables a study of sensitive parameters influencing the measurement uncertainties.

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