Towards virtual twin for electronic packages in automotive applications

Alexandru Prisacaru, Ernesto Oquelis Guerrero, Balakrishna Chimmineni, Przemyslaw Jakub Gromala, Yu-Hsiang Yang, Bongtae Han, Guo Qi Zhang

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The piezoresistive silicon based stress sensor has the potential to be part of the Digital Twin implementation in automotive electronics. One solution to enforce reliability in digital twins is the use of Machine Learning (ML). One or more physical parameters are being monitored, while other parameters are projected with surrogate models, just like virtual sensors. Piezo-resistive stress sensors are employed to measure the internal stresses of electronic packages, an Acquisition Unit (AU) to read out sensor data and a Raspberry Pi to perform evaluation. Accelerated tests in air thermal chamber are performed to get time series data of the stress sensor signals, with which we can know better about how delamination develops inside the package. In this study stress measurements are performed in several electronic packages during the delamination. The delamination is detected by the stress sensor due to the continuous change of the stiffness and the local boundary conditions causing the stresses to change. Although, the stress change in multiple cells can give enough information if it is delaminated or not, its delamination area location is unknown. Surrogate models built upon Neural Networks (NN) and Finite Element Method (FEM) are developed to predict the out of plane stresses at the delaminated layer. FEM simulation models are calibrated with Moiré measurements and validated at the component and PCB level with stress difference measurements. Simulation delamination areas are constructed based on the Scanning Acoustic Microscope (SAM) images, and are also validated with the equivalent stress measurements. In the end the surrogate model is predicting the out of plane stress in the adhesive layer. The results show good correlation when compared to the SAM images.

Original languageEnglish
Article number114134
Number of pages11
JournalMicroelectronics Reliability
Volume122
DOIs
Publication statusPublished - 2021

Keywords

  • Digital twin
  • Electronic package
  • Finite element method
  • Machine learning
  • Mechanical stress sensor
  • Surrogate modelling

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