Stress Prognostics for Encapsulated Standard Packages by Neural Networks Using Data from in-situ Condition Monitoring during Thermal Shock Tests

The prediction of high-resolution mechanical stress distributions in electronic chips with a view to improving prognostic and health management in electronics and N/MEMS via artificial intelligence-based processing of measurement data is the focus of this study. Temperature, shear, and differential stress time-series data acquired through piezo-resistive silicon-based stress sensors on multiple electronic packages inside a thermal shock chamber were monitored, recorded, and subsequently analyzed by various neural network models to create a better understanding and prediction of the delamination progress. Monitoring stress changes via continuous observation of material stiffness and interface integrity as factors influencing the local boundary conditions on chip cells, conveys pivotal information concerning the degradation progression. Deep neural networks empowered by backpropagation were trained to predict the stress distributions and ultimately monitor the degradation based on time-series data and were subsequently assessed for their performance to reliably predict in-plane stress developments and distributions on chips. In this study, long-short-term-memory- and gated-recurrent-unit-based networks could accurately predict the behavior of a single chip with smallest error.