Study on Data Effect of Using RNN Model to Predict Reliability Life of Wafer Level Packaging

In the field of electronic packaging, the main trend is toward lighter, smaller and more functional. In the evolution of packaging technology, from PTH(Pin Through Hole), SMT(Surface Mount Technology), BGA (Ball Grid Array) to higher density and smaller package like Flip Chip (FC) and Wafer Level Packaging (WLP).One of the important issues in the package structure is reliability. There are many factors that affect its reliability. For example, package structure size, material properties, process technology, etc. are all influencing factors. In the process of designing products, the experimental method is usually used, such as the Thermal Cycling Test, to test whether the designed product can pass the fatigue life standard, and in this way ensures that the designed product will not fail during the service period. However, in practice, if the reliability test is executed by experiment method, the cost and time spent will be quite high, resulting in delayed product launch time and rising development costs, even further caused a significant reduction in the competitiveness of enterprises.In order to improve the above shortcomings, Finite Element Analysis (FEA) has been widely used in reliability assessment. Only a small amount of experimental data is needed to verified the simulation then we can greatly reduce the time spent and cost for evaluating product reliability. In this study, ANSYS software was used to simulate the equivalent plastic strain increment in Wafer-Level Package under Accelerated thermal cycling load. The Coffin-Manson formula based on strain and fixed mesh size were used to predict the fatigue life of the solder ball. Finally, the simulation results are compared with the simulation results to verify the simulation analysis and its credibility.Although using the Finite Element Analysis (FEA) has reduced a lot of time spent and cost compared to Design on Experiment (DOE). The simulation results obtained by different researchers are not totally same. The purpose of this study is to apply Machine Learning (ML) to assess the reliability of electronic package structure. It can not only reduce the time spent for predicting the reliability prediction of different structures, but also eliminate the variance of the results simulated by different researchers. In this study, combined with finite element analysis and machine learning, a database built by verified simulation results, and the estimation model was trained through this database, making the reliability life prediction process more efficient.In the choice of machine learning model, this study chose to use the Recurrent Neural Network (RNN) and compare it with the basic Artificial Neural Network (ANN). The effects of different training data sizes and distributions on the performance of the regression model are also discussed.