Abstract
With the advancement of technology and consumers' demand for electronic products, electronic packaging technology is developing towards smaller sizes, lighter and thinner, and higher performance. Before entering the market, electronic packaging products will undergo accelerated thermal cycling (Thermal Cycling Test) reliability tests [1, 2]. The disadvantage of experimental detection is that it takes a lot of time and human capital. A verified finite element analysis model is used in the simulation analysis of reliability to improve the development time. However, the models established by different researchers often get different results. To eliminate simulation errors and reduce the difficulty of operation, this research combines the application of the Gaussian process regression model for data training to predict the Wafer Level Chip Scale Packaging (WLCSP) reliability. This study will explore the data effect of the Gaussian regression model on different kernel functions. The study will also combine K-Means clustering with an analysis of the time complexity of the Gaussian regression model based on the amount of data.