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Apr 12, 2017

在新产品验证过程中用 JMP® 脚本化统计模型估计过程能力_Henry Qiao, GE

 
演讲嘉宾:Henry Qiao,通用电气,质量经理
Speaker:Henry Qiao, Quality Manager, General Electric

 


主题:在新产品验证过程中用 JMP® 脚本化统计模型估计过程能力
Topics: A JMP® Scripted Modeling Approach to Estimate Process Capability in the Product Validation Process

 

 

摘要:

在FPGA(现场可编程门阵列)新产品验证过程中,常用多因子交叉试验的特性测试来估计长期过程能力。由于过程能力的估计是基于某一个特定的正态分布,不能直接用于多因子交叉试验的数据。本文旨在介绍一种新的模型方法来估计FPGA的过程能力。该方法首先建立一个统计模型获得过程输出的最差值,同时估计过程变异的大小,然后根据最差值计算过程能力的大小。所以,该方法获得的过程能力指数不是基于单个正态分布,而是根据多个正态分布数据获得的最差过程能力。本文也介绍了如何使用JMP脚本来自动优化统计模型及标准化数据分析的整个过程。该方法不仅适用于FPGA的产品验证过程中,实际上可用于任何半导体行业类似的数据分析。


 
Abstract:
In the product validation process of a Field Programmable Gate Array (FPGA) device, cross-factored characterization testing should be prescribed to estimate the effect of long-term process drifting. Process capability is usually estimated on the basis of one normal distribution. But the calculation cannot be applied to the cross-factored experiment data directly. This paper introduces a novel modeling approach to estimate the process capability of FPGAs. A statistical model is built to predict the worst-case values and estimate the inherent process variation, then calculate process capability indices. Therefore, the process capability indices are not obtained on the basis of one normal distribution. They actually show the worst process capability based on the cross-factored experiment data. An automated tool coded with JMP scripting language was also developed to optimize the model automatically, and to standardize the data process. This method can be applied not only to the data in the FPGA product validation process, but to all similar data in the semiconductor industry.

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