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

页码：237 / 241

页数：5

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