Quality Capability Assessment for Thin-Film Chip Resistor

Chen, Kuen‐Suan; Yang, Chun-Ming

doi:10.1109/access.2019.2927657

Cited by 6 publications

(3 citation statements)

References 20 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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“…Process capability indices (PCIs) are tools for processquality evaluation commonly applied in the industry. They can help manufacturers evaluate the process quality of their production processes and serve as an effective and convenient means of communication between internal engineers [11][12][13]. Six Sigma, developed in 1986 by Motorola, is widely applied to enhance product quality levels in the manufacturing industry [5,[14][15][16].…”

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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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“…In the second section, this study introduces the process improvement capability index, applies the methods of Chen and Yang [19] and Chen et al [20], takes the process improvement capability index as the objective function, regards the joint confidence region of accuracy index and precision index as a constraint, and obtains the upper confidence limit of the process improvement capability index through mathematical planning. Next, in the third section, a fuzzy membership function is constructed according to the upper confidence limit of the index.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Evaluation Model of Process Improvement Capability with Costs Consideration

Chen

Hsu

et al. 2021

Applied Sciences

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The Taguchi capability index, which reflects the expected loss and the yield of a process, is a useful index for evaluating the quality of a process. Several scholars have proposed a process improvement capability index based on the expected value of the Taguchi loss function as well as the corresponding cost of process improvement. There have been a number of studies using the Taguchi capability index to develop suppliers’ process quality evaluation models, whereas models for evaluating suppliers’ process improvement potential have been relatively lacking. Thus, this study applies the process improvement capability index to develop an evaluation model of the supplier’s process improvement capability, which can be provided to the industry for application. Besides, owing to the current need to respond quickly, coupled with cost considerations and the limits of technical capabilities, the sample size for sampling testing is usually not large. Consequently, the evaluation model of the process improvement capability developed in this study adopts a fuzzy testing method based on the confidence interval. This method reduces the risk of misjudgment due to sampling errors and improves the testing accuracy because it can incorporate experts and their accumulated experiences.

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Quality Capability Assessment for Thin-Film Chip Resistor

Cited by 6 publications

References 20 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

Fuzzy Evaluation Model of Process Improvement Capability with Costs Consideration

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