Redistribution Layer Defect Classification Using Computer Vision Techniques And Machine Learning

被引：1
|
作者：
Dangayach, Sachin [1 ]
Lianto, Prayudi [2 ]
Mishra, Satwik Swarup [1 ]
机构：
[1] Appl Mat Inc, Data Sci, Global Informat Serv, Bangalore, Karnataka, India
[2] Appl Mat Inc, Adv Packaging Dev Ctr, 10 Sci Pk Rd, Singapore, Singapore
来源：
2020 IEEE 22ND ELECTRONICS PACKAGING TECHNOLOGY CONFERENCE (EPTC) | 2020年
关键词：
image processing; machine learning; data analytics; advanced metrology; defect binning; RDL;
D O I：
10.1109/EPTC50525.2020.9315117
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In the semiconductor industry, defects are yield killers and the detection/classification of which can be expensive as well as time consuming. To overcome this challenge, we propose a solution involving Computer Vision Techniques and Machine Learning to accomplish defect binning procedure in typical wafer-level packaging scenario, focusing on 2um L/S redistribution layer (RDL) features. With this approach, inspection cycle time is reduced, thereby driving faster product development.
引用
收藏
页码：237 / 241
页数：5
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