Predicting Wafer-Level Package Reliability Life Using Mixed Supervised and Unsupervised Machine Learning Algorithms

Materials

Qing-Hua Su¹  Kuo-Ning Chiang¹ 

[1] Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan;

关键词: Wafer-Level Package (WLP);    Finite Element Analysis (FEA);    machine learning;    Kernel Ridge Regression (KRR);    Cluster algorithm;   

DOI  :  10.3390/ma15113897

来源: DOAJ

【 摘 要 】

With the increasing demand for electronic products, the electronic package gradually developed toward miniaturization and high density. The most significant advantage of the Wafer-Level Package (WLP) is that it can effectively reduce the volume and footprint area of the package. An important issue in the design of WLP is how to quickly and accurately predict the reliability life under the accelerated thermal cycling test (ATCT). If the simulation approach is not adopted, it usually takes several ACTCs to design a WLP, and each ACTC will take several months to get the reliability life results, which increases development time considerably. However, simulation results may differ depending on the designer’s domain knowledge, ability, and experience. This shortcoming can be overcome with artificial intelligence (AI). In this study, finite element analysis (FEA) is combined with machine learning algorithms, e.g., Kernel Ridge Regression (KRR), to create an AI model for predicting the reliability life of electronic packaging. Kernel Ridge Regression (KRR) combined with the K-means cluster algorithm provides a highly accurate and efficient way to obtain AI models for large-scale data sets.

【 授权许可】

Unknown