Application of RPN analysis to parameter optimization of passive components

Chen, K.S.; Wang, C.C.; Wang, C.H.; Huang, C.F.

doi:10.1016/j.microrel.2010.06.014

Cited by 20 publications

(6 citation statements)

References 16 publications

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“…According to a number of studies, process capability indices (PCIs) are convenient tools for process quality assessment, such that they are widely employed in the manufacturing industry [1][2][3][4][5][6][7][8]. Six Sigma is also a widely-used approach which can enhance process quality levels in manufacturing [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

Luo

Hsu

et al. 2020

Mathematics

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The quality characteristics with unilateral specifications include the smaller-the-better (STB) and larger-the-better (LTB) quality characteristics. Roundness, verticality, and concentricity are categorized into the STB quality characteristics, while the wire pull and the ball shear of gold wire bonding are categorized into the LTB quality characteristics. In terms of the tolerance, zero and infinity () can be viewed as the target values in line with the STB and LTB quality characteristics, respectively. However, cost and timeliness considerations, or the restrictions of practical technical capabilities in the industry, mean that the process mean is generally far more than 1.5 standard deviations away from the target value. Researchers have accordingly proposed a process quality index conforming to the STB quality characteristics. In this study, we come up with a process quality index conforming to the LTB quality characteristics. We refer to these two types of indices as the unilateral specification process quality indices. These indices and the process yield have a one-to-one mathematical relationship. Besides, the process quality levels can be completely reflected as well. These indices possess unknown parameters. Therefore, sample data are required for calculation. Nevertheless, interval estimates can lower the misjudgment risk resulting from sampling errors more than point estimates can. In addition, considering cost and timeliness in the industry, samples are generally small, which lowers estimation accuracy. In an attempt to increase the accuracy of estimation as well as overcome the uncertainty of measured data, we first derive the confidence interval for unilateral specification process quality indices, and then propose a fuzzy membership function on the basis of the confidence interval to establish the two-tailed fuzzy testing rules for a single indicator. Lastly, we determine whether the process quality has improved.

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Section: Introductionmentioning

confidence: 99%

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

Luo

Hsu

et al. 2020

Mathematics

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“…We therefore propose a fuzzy process-quality evaluation model for wire bonding in IC packaging. Fuzzy evaluation models not only assess process quality but also identify which critical-to-quality (CTQ) characteristics require improvement [7][8][9][10]. Process capability indices (PCIs) are tools for processquality evaluation commonly applied in the industry.…”

Section: Fig 1: Ic Packaging Flow Chartmentioning

confidence: 99%

Process-Quality Evaluation for Wire Bonding With Multiple Gold Wires

Lai

Chen

et al. 2020

IEEE Access

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Taiwan is a world-leader in wafer foundry services and IC packaging and testing. Wire bonding is a crucial process in the overall IC-packaging industry chain. Thus, this paper proposes a processquality evaluation model for wire bonding with multiple gold wires. We chose process quality indices as a tool of evaluation fully mirroring process yield and quality levels. These indices contain unknown parameters and thus require sample data to estimate. We first derived the uniformly minimum variance unbiased estimator of the indices and calculated the upper confidence limits of the indices based on DeMorgan's theorem and Boole's inequality. The upper confidence limits of the indices were then employed to create a confidence interval-based fuzzy membership function, in order to improve the accuracy of estimation as well as solve the problem of uncertainty of the measured data. Next, we obtained the fuzzy critical value and used index estimates and the fuzzy critical value to establish fuzzy test rules. Next, we marked the fuzzy critical value on the axes of a radar chart, which is a visualization evaluation tool, and connected neighboring critical points to create a critical region in the form of a regular polygon. The observed values of the indices were then marked on the axes to produce a visualized fuzzy radar evaluation chart. This fuzzy radar evaluation chart has a solid foundation in statistical inference, and evaluation rules were established using precise fuzzy test methods. Not only is this fuzzy radar evaluation chart easy to use, but it also reduces the chance of misinterpretations made by sampling errors, so that the accuracy of evaluation can be enhanced. INDEX TERMS process quality index, fuzzy critical value, critical region, wire bonding, radar chart.

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“…The FMEA model has now been widely used in many industries (Wang et al, 2018;Ouyang et al, 2022). For example, some semiconductor manufacturing industries have also introduced FMEA system evaluation operations into the production line management to avoid potential major anomalies and enhance product reliability (Chen et al, 2010). In addition, numerous mass transportation system engineering and construction projects should also use FMEA to prevent and reduce failures in engineering and systems as well as lower the losses caused by system failures (Ng et al, 2022;Gumasing et al, 2022;Gong et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Application of Statistical Data and Methods to Establish RPN Ratings of Fmea Method for Construction Projects

Juan,

Sheu,

Chen

2023

Journal of Civil Engineering and Management

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The Failure Mode and Effects Analysis (FMEA) is paramount for analytical skills of reliability design in dynamic prevention. The FMEA model is a significant method which can simultaneously reduce the operating errors or delays as well as improve the construction quality. In particular, the Risk Priority Number (RPN) in the FMEA model is a vital tool which helps construction managers prioritize problem-solving. As the Internet of Things and big data analytical skills have become progressively widespread and mature, among the three risk indicators of RPN, the number of operating errors or delays per unit time can be estimated by the data collected from the analysis of statistical methods and regarded as the basis of 10-level classification. In addition, when the loss is larger, then the severity is higher. This paper proposed three evaluation criteria, including Occurrence, Severity, and Detection of RPN in construction engineering, and a 10-level classification model. To assist the construction managers, priority for construction improvement can be identified based on RPN calculations.

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Application of RPN analysis to parameter optimization of passive components

Cited by 20 publications

References 16 publications

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

Process-Quality Evaluation for Wire Bonding With Multiple Gold Wires

Application of Statistical Data and Methods to Establish RPN Ratings of Fmea Method for Construction Projects

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