• 산업경영시스템학회지
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• 제39권1호
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• pp.91-97
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• 2016

This study is concerned about the process capability index in single process. Previous process capability indices have been developed for the consistency with the nonconforming rate due to the process target value and skewness. These indices calculate the process capability by measuring one spot in an item. But the only one datum in an item reduces the representativeness of the item. In addition to the lack of representativeness, there are many cases that the uniformity of the item such as flatness of panel is absolutely important. In these cases, we have to measure several spots in an item. Also the nonconforming judgment to an item is mainly due to the range not due to the standard variation or the shift from the specifications. To imply the uniformity concept to the process capability index, we should consider only the variation in an item. It is the within subgroup variation. When the universe is composed of several subgroups, the sample standard deviation is the sum of the within subgroup variation and the between subgroup variation. So the range R which represents only the within subgroup variation is the much better measure than that of the sample standard deviation. In general, a subgroup contains a couple of individual items. But in our cases, a subgroup is an item and R is the difference between the maximum and the minimum among the measured data in an item. Even though our object is a single process index, causing by the subgroups, its analytic structure looks like a system process capability index. In this paper we propose a new process capability index considering the representativeness and uniformity.

• 한국기계가공학회지
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• 제10권6호
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• pp.16-21
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• 2011

In machining operation, the quality of surface finish is an important factor for many turned products. In this paper, surface quality in turning machining considering angle variation has been investigated. To reach this goal, surface quality turning experiments are carried out according to cutting conditions with angle variation. The variable cutting conditions are cutting speed, feed rate and taper angle of workpiece. The surface roughness was measured and the effects of cutting conditions were analyzed by the method of analysis of variance (ANOVA). From the experimental results and ANOVA, it is found that a better surface roughness can be obtained as decreasing feed rate, increasing cutting speed. Taper angle variation has been more influenced by feed rate and cutting speed.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권2호
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• pp.184-193
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• 2015

This paper proposes a cost-efficient and automatic method for large data acquisition from a test chip without expensive equipment to characterize random process variation in an integrated circuit. Our method requires only a test chip, a personal computer, a cheap digital-to-analog converter, a controller and multimeters, and thus large volume measurement can be performed on an office desk at low cost. To demonstrate the proposed method, we designed a test chip with a current model logic driver and an array of 128 current mirrors that mimic the random process variation of the driver's tail current mirror. Using our method, we characterized the random process variation of the driver's voltage due to the random process variation on the driver's tail current mirror from large volume measurement data. The statistical characteristics of the driver's output voltage calculated from the measured data are compared with Monte Carlo simulation. The difference between the measured and the simulated averages and standard deviations are less than 20% showing that we can easily characterize the random process variation at low cost by using our cost-efficient automatic large data acquisition method.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.975-982
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• 2005

The purpose of the Gage R&R study is to determine whether a measurement system is adequate for monitoring a process. If the measurement system variation is small relative to the process variation, then the measurement system is deemed 'adequate'. The sources of variation associated with the measurement system are compared using an analysis of variance (ANOVA) model, in general. A typical ANOVA model used in a standard Gage R&R study is the two-factor random effect model. Then, the ANOVA partitions the total variation into three categories: repeatability, reproducibility, part variation. However, if the process variation possesses the between group variation, within group variation, and within-part variation, these variations can cause the measurement system evaluation to provide misleading results. That is, in the standard Gage R&R study these variations affect the estimate of repeatability, reproducibility, or both. This paper presents a four-factor nested factorial ANOVA model which explicitly considers these variations for the Gage R&R study. The variance component estimates are derived by setting the EMS equations equal to the corresponding mean square from the ANOVA table and solving. And the proposed model is compared with the standard Gage R&R model.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2007년도 춘계학술대회
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• pp.287-296
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• 2007

Pearn et al.(2002) supposed the $C_{pp}$ multiple process performance analysis chart. This chart display multiple processes with the process variation and process departure on one single chart. But, this chart can not display the distribution of the process variation and process departure. With bootstrapping method, we can display the distribution of the process variation and process departure on the $C_{pp}$ multiple process performance analysis chart.

• 품질경영학회지
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• 제22권4호
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• pp.79-101
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• 1994

In modern manufacturing, a product consists of many components created by different processes. Variations in the individual component dimensions and in the processes may result in unacceptable final assemblies. Thus, engineers have increased pressure to properly set tolerance specifications for individual components and to control manufacturing processes. When a proper variation stack-up analysis is not performed for all of the components in a functional system, all component parts can be within specifications, but the final assembly may not be functional. Thus, in order to improve the performance of the final assembly, a proper variation stack-up analysis is essential for specifying dimensional tolerances and process control. This research provides a detailed case example of the use of variation stack-up analysis using a Monte Carlo simulation method to improve the defect rate of a complex process, which is the commutator brush track undercut process of an armature assembly of a small motor. Variations in individual component dimensions and process mean shifts cause high defect rate, Since some dimensional characteristics have non-normal distributions and the stack-up function is non-linear, the Monte Carlo simulation method is used.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권6호
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• pp.678-684
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• 2015

Near-threshold computing (NTC) is now regarded as a promising candidate for innovative power reduction, which cannot be achieved with conventional super-threshold computing (STC). However, performance degradation and vulnerability to process variation in the NTC regime are the primary concerns. In this paper, we propose a voltage- and frequency-tuning methodology for mitigating the process-variation-induced problems in NTC-based manycore architectures. To implement the proposed methodology, we build up multiple-voltage multiple-frequency (MVMF) islands and apply a voltage-frequency tuning algorithm based on the critical-path monitoring technique to reduce the effects of process variation and maximize energy efficiency in the post-silicon stage. Experimental results show that the proposed methodology reduces overall power consumption by 8.2-20.0%, compared to existing methods in variation-sensitive NTC environments.

• E2M - 전기 전자와 첨단 소재
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• 제9권8호
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• pp.799-805
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• 1996

In short channel MOSFET, it is very important to establish optimal process conditions because of variation of device characteristics due to the process parameters. In this paper, we used process simulator and device characteristics caused by process parameter variation. From this simulation, it has been ' derived to the dependence relations between process parameters and device characteristics. The experimental result of fabricated short channel device according to the optimal process parameters demonstrate good device characteristics.

• 한국전기전자재료학회논문지
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• 제24권5호
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• pp.387-391
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• 2011

A uncooled infrared ray sensor used in an infrared thermal imaging detector has many advantages. But because the uncooled infrared ray sensor is made by MEMS (micro-electro-mechanical system) process variation of offset is large. In this paper, to solve process variation of offset a ROIC for uncooled infrared ray sensor that has process variation of offset compensation technique using differential delta sampling and reference signal compensation circuit was proposed. As a result of simulation that uses the proposed ROIC, it was possible to acquire compensated output characteristics without process variation of offsets.

• 산업공학
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• 제16권3호
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• pp.344-351
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• 2003

Critical dimension is one of the most important characteristics of up-to-date integrated circuit devices. Hence, critical dimension control in a semiconductor wafer fabrication process is inevitable in order to achieve optimum device yield as well as electrically specified functions. Currently, in complex semiconductor wafer fabrication processes, statistical methodologies such as Shewhart-type control charts become crucial tools for practitioners. Meanwhile, given a critical dimension sampling plan, the analysis of variance technique can be more effective to investigating critical dimension variation, especially for on-chip and on-wafer variation. In this paper, relating to a typical sampling plan, linear statistical models are presented for the analysis of critical dimension variation. A case study is illustrated regarding a semiconductor wafer fabrication process.