Predict the Reliability Life of Wafer Level Packaging using K-Nearest Neighbors algorithm with Cluster Analysis

Moore’s law was proposed by Gordon Earle Moore, who believes that the number of transistors that can be accommodated on an integrated circuit would double about every 18 months. Since it is approaching the physical limit, Moore’s law is no longer applicable. Packaging technology becomes more important in the post Moore era. The development of electronic packaging can be roughly divided into five stages, namely TO-CAN, DIP (Dual In-line Package), PQFP (Plastic Quad Flat Pack), PBGA (Plastic Ball Grid Array) and the CSP (Chip Scale Package) used in this research. The evolutions are to improve signal transmission speed, storage capacity and the pursuit of higher packaging density. The reliability of packages is very important. Different sizes or manufacturing methods will affect their lifetime. Before these packages are put on the market, they must be tested and experimented to ensure their reliability. However, it will waste a lot of resources and time costs, resulting in less profit.Finite element analysis is a numerical method that can subdivide a large physical system into a finite number of smaller and simpler elements. The study uses ANSYS to simulate WLCSP (Wafer Level Chip Scale Packaging) through thermal cycling test, and used empirical formulas to estimate the lifetime of solder balls. Also, the mesh size at the maximum DNP (Distance from Neutral Point) is fixed. Make the simulation closer to the experiment results. After the verification of simulation and experimental data, the feasibility of the model is established, thereby saving the huge time cost of packaging testing and experimentation.However, finite element analysis will produce different results depending on the researcher. In order to avoid this factor and save the time spent in constructing the model, this research introduces artificial intelligence and combines supervised learning and unsupervised learning to estimate the solder ball lifetime. In this study, we used the verified finite element model to obtain different lifetime according to different sizes, and then introduced a large amount of data into AI algorithms [1] to achieve the purpose of quickly predicting the reliability of the package.The algorithm used in this study is KNN (K-Nearest Neighbors) which can be used for classification and regression, and uses different data numbers, different preprocessing methods, different distance definitions, and different weighting methods to compare the impact of the algorithm’s predictions on the lifetime of our packages. In addition, we combine unsupervised learning methods like K-means to assign data of the same characteristic into each cluster. Try to simplify the complexity of the model, save calculation time and improve the performance of KNN.