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Base on bias and process capability index monitoring mechanism to improve tool consistency

机译:基于偏差和过程能力指标的监视机制,以提高工具的一致性

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The monitoring mechanism uses in-line data which is conservative stratification and grouping to calculation %Bias. (%Bias=bias/process variation.) It represents tools are significant difference when %Bias>10% referring to MSA 2nd manual. As each tool is too stable, the influence of variation would be so sensitive, and the mechanism would be too strict. We developed if we combined %Bias criteria of MSA 2nd manual and Cpk monitor in order to solve the sensitive variation of the stable process. Therefore, this mechanism adds Cpk criterion. Due to the method focuses on tools consistency, the Ca formula is made an amendment to measure the degree between difference tools. In general, the criterion of Cpk is 1.67 that means process capability is capable. In the mechanism, the criterion of Cpk is tightened to 1.83. This paper discusses an inline monitoring mechanism to control tools consistency. We use Analysis of variance (ANOVA) test to prove the bias of tools that needs to improve. Therefore, we combined %Bias and Cpk to monitor in our mechanism. After improving the mechanism, it could solve the lack of the past mechanism. Finally, the mechanism can be immediate, comprehensive and effective to control and meet quality requirements.
机译:监视机制使用保守的分层和分组的在线数据来计算%Bias。 (%Bias =偏差/过程偏差。)它表示工具在参考MSA 2 nd 的%Bias> 10%时具有显着差异。由于每个工具都太稳定,因此变化的影响会非常敏感,机制也会过于严格。如果我们结合MSA 2 nd 手册的%Bias准则和Cpk监视器来开发,以解决稳定过程的敏感变化。因此,此机制添加了Cpk准则。由于该方法侧重于工具一致性,因此对Ca公式进行了修正以测量不同工具之间的程度。通常,Cpk的标准为1.67,这意味着过程能力是有能力的。在该机制中,Cpk的标准严格到1.83。本文讨论了一种在线监控机制来控制工具的一致性。我们使用方差分析(ANOVA)检验来证明需要改进的工具的偏见。因此,我们结合了%Bias和Cpk来监视我们的机制。改进机制后,可以解决过去机制的不足。最后,该机制可以即时,全面,有效地控制和满足质量要求。

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