Quality Control, Robust Design, and the Taguchi Method 1989
DOI: 10.1007/978-1-4684-1472-1_6
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Off-Line Quality Control in Integrated Circuit Fabrication using Experimental Design

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Cited by 59 publications
(43 citation statements)
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“…In such a case the performance value corresponding to optimum working conditions can be predicted by utilizing the balanced characteristic of OA. For this, the additive model may be used (Phadke, 1983): where m is the overall mean of performance value, X i the fixed effect of the parameter level combination used in i th experiment, and e i the random error in i th experiment. Because Equation (4) is a point estimation, which is calculated by using experimental data in order to determine whether results of the confirmation experiments are meaningful or not, the confidence interval must be evaluated.…”
Section: Methodsmentioning
confidence: 99%
“…In such a case the performance value corresponding to optimum working conditions can be predicted by utilizing the balanced characteristic of OA. For this, the additive model may be used (Phadke, 1983): where m is the overall mean of performance value, X i the fixed effect of the parameter level combination used in i th experiment, and e i the random error in i th experiment. Because Equation (4) is a point estimation, which is calculated by using experimental data in order to determine whether results of the confirmation experiments are meaningful or not, the confidence interval must be evaluated.…”
Section: Methodsmentioning
confidence: 99%
“…In such cases, the performance value corresponding to optimum working conditions can be predicted by utilizing the balanced characteristic of the OA. For this aim, the additive model may be used [7] …”
Section: Experimental Parameters and Planmentioning
confidence: 99%
“…In the Taguchi method, if the experiment corresponding to optimum working conditions is not present in the experiment plan, the performance value can be estimated with the help of the following equation [22].…”
mentioning
confidence: 99%
“…Since Equation (6) is an estimation calculated by using experimental data in order to determine whether the additional model is appropriate, the confidence interval for the estimated error needs to be evaluated [22]. The estimated error is the difference between the observed p and the estimated p. The confidence interval (S e ) for the estimated error is found with the help of the following equation.…”
mentioning
confidence: 99%
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