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 of the failure behavior over time including failure mechanisms, the delamination process in particular and the related stress distribution. 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.

本研究的重点是预测电子芯片中的高分辨率机械应力分布，以通过基于人工智能的测量数据处理来改善电子和 N/MEMS 的预测和健康管理。通过热冲击室内多个电子封装上的压阻硅基应力传感器获取的温度、剪切和微分应力时间序列数据被监测、记录，随后由各种神经网络模型进行分析，以更好地了解随时间推移的失效行为，包括失效机制、特别是分层过程以及相关的应力分布。通过监测压力变化连续观察材料刚度和界面完整性作为影响芯片单元局部边界条件的因素，传达了有关退化进程的关键信息。由反向传播赋能的深度神经网络经过训练以预测应力分布并最终根据时间序列数据监测退化情况，并随后对其性能进行评估，以可靠地预测芯片上的平面应力发展和分布。在这项研究中，基于长短期记忆和门控循环单元的网络可以以最小的误差准确预测单个芯片的行为。