Mining Factors Impact Wafer Circuit Probing Via Neural Network and Statistics for Semiconductor Device Fabrication

J. Kung, Yung Chien Kung, Jing Pei Lin, Ming Wei Chen, Te Hsuan Chen, Hsiao Ying Yang, Pei Wen Chen
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Abstract

Wafer circuit probing (CP) testing is one of the most important processes for semiconductor manufacturing to ensure the wafers are of good quality. However, the outcomes of CP measurements are not always as good as expected. Engineers take lots of efforts to diagnose CP measurement and classify the features so that make the root cause more evident. In searching the root cause for low yield wafers, gathering the bad wafers and finding their correlation between yields and stages for each process is a common procedure to check whether the stage or the corresponding equipment is the one of the factors that lowers the yield significantly. A system was developed to realize the procedure that defines wafer status and points out the problems that make the yield lower. Once a wafer was inputted into this system, a diagnosis for the wafer will be made automatically. And hence this system was named auto commonality, which means grouping the bad wafers and finding the root cause then making decisions without any manpower.
基于神经网络和统计的半导体器件制造晶圆电路探测挖掘因素
晶圆电路探测(CP)测试是半导体制造中保证晶圆质量的重要过程之一。然而,CP测量的结果并不总是像预期的那样好。工程师花费大量的精力来诊断CP测量并对特征进行分类,以便使根本原因更加明显。在寻找低良率晶圆的根本原因时,收集不良晶圆,找出每道工艺的良率与工艺阶段之间的相关性,是检验工艺阶段或相应的设备是否是显著降低良率的因素之一的常用程序。开发了一个系统,实现了晶圆状态的定义和晶圆良率问题的指出程序。一旦晶圆片被输入到这个系统中,就会自动对晶圆片进行诊断。因此,这个系统被命名为自动共通,即将不良晶圆进行分组,找出根本原因,然后在没有任何人力的情况下做出决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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