In-plant Applications Research Articles

C pm MPPAC for manufacturing quality control applied to precision voltage reference process

A multiprocess performance analysis chart (MPPAC), based on the process capability index Cpm, called Cpm MPPAC, is developed to analyse the manufacturing quality of a group of processes in a multiple process environment. The Cpm MPPAC conveys critical information about multiple processes regarding the departure of the process and process variability on one single chart. Existing research on MPPAC has been restricted to obtaining quality information from one single sample of each process, ignoring sampling errors. The information provided from the existing MPPAC chart, therefore, is unreliable and misleading, resulting in incorrect decisions. In this paper, the natural estimator of Cpmis considered based on multiple samples. Based on the natural estimator of Cpm, sampling errors are considered by providing an explicit formula with Matlab to obtain the estimation accuracy of the Cpm. The sampling accuracy of Cpmis tablulated for sample size determination so that engineers/practitioners can use it for in-plant applications. An example of multiple PVR processes is presented to illustrate the applicability of Cpm MPPAC for manufacturing quality control.

Measuring manufacturing capability based on lower confidence bounds of C pmk applied to current transmitter process

Several process capability indices, including Cp, Cpk, and Cpm, have been proposed to provide numerical measures on manufacturing potential and actual performance. Combining the advantages of those indices, a more advanced index Cpmk is proposed, taking the process variation, centre of the specification tolerance, and the proximity to the target value into account, which has been shown to be a useful capability index for manufacturing processes with two-sided specification limits. In this paper, we consider the estimation of Cpmk, and we develop an efficient algorithm to compute the lower confidence bounds on Cpmk based on the estimation, which presents a measure on the minimum manufacturing capability of the process based on the sample data. We also provide tables for practitioners to use in measuring their processes. A real-world example of current transmitters taken from a microelectronics device manufacturing process is investigated to illustrate the applicability of the proposed approach. Our implementation of the existing statistical theory for manufacturing capability assessment bridges the gap between the theoretical development and the in-plant applications.

An algorithm for calculating the lower confidence bounds of CPU and CPL with application to low-drop-out linear regulators

In assessing the performance of normal stable manufacturing processes with one-sided specification limits, process capability indices C PU and C PL have been widely used to measure the process capability. The purpose of this paper is to develop an algorithm to compute the lower confidence bounds on C PU and C PL using the UMVUEs of C PU and C PL. The lower confidence bound presents a measure on the minimum capability of the process based on the sample data. We also provide tables for the engineers/practitioners to use in measuring their processes. A real-world example taken from a microelectronics device manufacturing process is investigated to illustrate the applicability of the algorithm. Implementation of the existing statistical theory for capability assessment fills the gap between the theoretical development and the in-plant applications.

Lower confidence bounds with sample size information for Cpm applied to production yield assurance

The process capability index C pm , sometimes called the Taguchi index, has been proposed to the manufacturing industry as providing numerical measures on process performance. A lower confidence bound estimates the minimum process capability, conveying critical information regarding product quality, which is essential to quality assurance. The sample size determination is directly related to the cost of the data collection plan. The purpose of this paper is to provide explicit formulas with efficient algorithms to obtain the lower confidence bounds and sample sizes required for specified precision of the estimation on C pm using the maximum likelihood estimator (MLE) of C pm . We also provide tables for the engineers/practitioners to use for their in-plant applications. A real-world example taken from a microelectronics manufacturing process is investigated to illustrate the applicability of the proposed approach. The implementation of existing statistical theory for capability assessment bridges the gap between the theoretical development and factory applications.

Testing process capability for one-sided specification limit with application to the voltage level translator

Process capability indices C PU and C PL have been widely used in the microelectronics manufacturing industry as capability measures for processes with one-sided specification limits. In this paper, the theory of statistical hypothesis testing is implemented for normal processes, using the uniformly minimum variance unbiased estimators of C PU and C PL. Efficient SAS computer programs are provided to calculate the critical values and the p-values required for making decisions. Useful critical values for some commonly used capability requirements are tabulated. Based on the test a simple but practical step-by-step procedure is developed for in-plant applications. An example on the voltage level translator manufacturing process is given to illustrate how the proposed procedure may be applied to test whether the process meets the preset capability requirement.