Intelligent diagnosis of the solder bumps defects using fuzzy C-means algorithm with the weighted coefficients

作     者：LU XiangNing SHI TieLin WANG SuYa LI Li Yi SU Lei LIAO GuangLan 

作者机构：School of Mechanical & Electrical Engineering Jiangsu Normal University School of Materials Science and Engineering Georgia Institute of Technology State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2015年第58卷第10期

页      面：1689-1695页

核心收录：

学科分类：080503[工学-材料加工工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.51305179&51305177) the Natural Science Foundation of Jiangsu Higher Education Institutions(Grant No.13KJB510009) 

主　　题：solder bump Fuzzy C-Means clustering feature weighting principal component analysis 

摘      要：Solder bump technology has been widely used in electronic packaging. With the development of solder bumps towards higher density and finer pitch, it is more difficult to inspect the defects of solder bumps as they are hidden in the package. A nondestructive method using the transient active thermography has been proposed to inspect the defects of a solder bump, and we aim at developing an intelligent diagnosis system to eliminate the influence of emissivity unevenness and non-uniform heating on defects recognition in active infrared testing. An improved fuzzy c-means(FCM) algorithm based on the entropy weights is investigated in this paper. The captured thermograms are preprocessed to enhance the thermal contrast between the defective and good bumps. Hot spots corresponding to 16 solder bumps are segmented from the thermal images. The statistical features are calculated and selected appropriately to characterize the status of solder bumps in FCM clustering. The missing bump is identified in the FCM result, which is also validated by the principle component analysis. The intelligent diagnosis system using FCM algorithm with the entropy weights is effective for defects recognition in electronic packages.