Solder joint reliability risk estimation by AI modeling
| Autor: | Chang-Chi Lee, Cadmus Yuan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science Reliability (computer networking) Computer Science::Neural and Evolutionary Computation 020208 electrical & electronic engineering 02 engineering and technology Finite element method Reliability engineering Task (computing) Nonlinear system Recurrent neural network Convergence (routing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Wafer-level packaging |
| Zdroj: | 2020 21st International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE). |
| Popis: | This paper studies AI modeling for the solder joint fatigue risk estimation under the thermal cycle loading of redistributed wafer level packaging. The artificial neural network (ANN), recurrent neural network (RNN) and vectorized-gate network long short-term memory (VNLSTM) architectures have been trained by the same dataset to investigate their performance for this task. The learning accuracy criterion, the implementation of all neural network architecture, the learning results and result analysis would be covered.Because the involvement of the time/temperature-dependent nonlinearity material characteristics, it is recommended that more than three hidden layers and a proper neural network architecture, which is capable of the sequential data processing, should be considered in order to guarantee the required accuracy and the satisfied convergence speed. |
| Databáze: | OpenAIRE |
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