基于计算机视觉和机器学习的再分配层缺陷分类

Sachin Dangayach, Prayudi Lianto, S. Mishra
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引用次数: 1

摘要

在半导体工业中,缺陷是产量杀手,其检测/分类可能既昂贵又耗时。为了克服这一挑战,我们提出了一种涉及计算机视觉技术和机器学习的解决方案,以完成典型晶圆级封装场景中的缺陷分类过程,重点关注2um L/S再分布层(RDL)特征。通过这种方法,减少了检查周期时间,从而推动了更快的产品开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Redistribution Layer Defect Classification Using Computer Vision Techniques And Machine Learning
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.
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