On Determination of Sample Size to Evaluate Reliability Growth Plans

2011 ◽  
Author(s):  
X. Jin ◽  
P. Woytowitz ◽  
T. Tan
Keyword(s):  
Sample Size ◽  
Confidence Level ◽  
Semiconductor Manufacturing ◽  
Field Performance ◽  
Reliability Growth ◽  
Confidence Levels ◽  
Equipment Reliability ◽  
Margin Of Error ◽  
Reliability Performance

The reliability performance of Semiconductor Manufacturing Equipments (SME) is very important for both equipment manufacturers and customers. However, the response variables are random in nature and can significantly change due to many factors. In order to track the equipment reliability performance with certain confidence, this paper proposes an efficient methodology to calculate the number of samples needed to measure the reliability performance of the SME tools. This paper presents a frequency-based Statistics methodology to calculate the number of sampled tools to evaluate the SME reliability field performance based on certain confidence levels and error margins. One example case has been investigated to demonstrate the method. We demonstrate that the multiple weeks accumulated average reliability metrics of multiple tools do not equal the average of the multiple weeks accumulated average reliability metrics of these tools. We show how the number of required sampled tools increases when the reliability performance is improved and quantify the larger number of sampled tools required when a tighter margin of error or higher confidence level is needed.

scholarly journals Determination of the Uncertainties in S-Curve Logistic Fits

2022 ◽  
Author(s):  
THEODORE MODIS
Keyword(s):  
General Result ◽  
Confidence Level ◽  
Simulated Data ◽  
Historical Period ◽  
Confidence Levels ◽  
Minimization Technique ◽  
Data Points ◽  
S Curve ◽  
Better Than

Look-up tables and graphs are provided for determining the uncertainties during logistic fits, on the three parameters M, α and to describing an S-curve of the form: S(t) = M/(1+exp(-α(t-t0))).The uncertainties and the associated confidence levels are given as a function of the uncertainty on the data points and the length of the historical period. Correlations between these variables are also examined; they make “what-if” games possible even before doing the fit.The study is based on some 35,000 S-curve fits on simulated data covering a variety of conditions and carried out via a χ2 minimization technique. A rule-of-thumb general result is that, given at least half of the S-curve range and a precision of better than 10% on each historical point, the uncertainty on M will be less than 20% with 90% confidence level.

DEFINITION OF TIME SPENT ON REPAIR OF BUS TRANSMISSION UNITS TAKING INTO ACCOUNT TECHNOLOGICAL EQUIPMENT RELIABILITY

2020 ◽  
Vol 2020 (9) ◽  
pp. 29-33
Author(s):  
Sergey Bulatov
Keyword(s):  
Negative Impact ◽  
Probabilistic Method ◽  
Technological Equipment ◽  
Quality And Safety ◽  
Transmission Unit ◽  
Equipment Reliability ◽  
Hours Of Work ◽  
Definition Of ◽  
Time Decrease

The paper purpose is the effectiveness estimation in the technological equipment use, taking into account its reliability and productivity for defective transmission units of buses. The problem consists in the determination of time to be spent on repair of bus transmission units taking into account technological equipment reliability. In the paper there is used a probabilistic method for the prediction bus transmission units, and also a method of the dynamics of averages which allow ensuring minimum of costs for units downtime during repair and equipment cost. The need for repair of transmission units (gear box) arises on an average after 650 hours, the average productivity of the bench makes 4.2 bus / hour. The bench fails on the average after 4600 hours of work, the average time of the bench makes 2 hours. In such a way the solution of the problem specified allows analyzing the necessity of time decrease for transmission unit repair to avoid long downtimes of buses in repair areas without negative impact upon high repair quality and safety during the further operation.

Smart Estimation Ver-H.1.0

2018 ◽  
Author(s):  
Sigit Haryadi
Keyword(s):  
Sample Size ◽  
Confidence Level ◽  
Time Span ◽  
Regression Equation ◽  
Weight Factor ◽  
The Past ◽  
Level Of Confidence ◽  
The Future ◽  
Future Value ◽  
The Difference

We cannot be sure exactly what will happen, we can only estimate by using a particular method, where each method must have the formula to create a regression equation and a formula to calculate the confidence level of the estimated value. This paper conveys a method of estimating the future values, in which the formula for creating a regression equation is based on the assumption that the future value will depend on the difference of the past values divided by a weight factor which corresponding to the time span to the present, and the formula for calculating the level of confidence is to use "the Haryadi Index". The advantage of this method is to remain accurate regardless of the sample size and may ignore the past value that is considered irrelevant.

Cryoscopy of Milk: Effect of Variations in the Method

1966 ◽  
Vol 49 (3) ◽  
pp. 511-515 ◽  
Author(s):  
R W Henningson
Keyword(s):  
Statistical Analysis ◽  
Sample Size ◽  
Freezing Point ◽  
Collaborative Study ◽  
Sample Temperature ◽  
Milk Samples ◽  
Two Samples ◽  
Temperature Rate

Abstract Bath level, sample temperature, rate of stirring, degree of supercooling, sample size, sample isolation, and refreezing of the sample were the variables in the thermistor cryoscopic method for the determination of the freezing point value of milk chosen for study. Freezing point values were determined for two samples of milk and two secondary salt standards utilizing eight combinations of the seven variables in two test patterns. The freezing point value of the salt standards ranged from –0.413 to –0.433°C and from –0.431 to –0.642°C. The freezing point values of the milk samples ranged from –0.502 to –0.544°C and from –0.518 to –0.550°C. Statistical analysis of the data showed that sample isolation was a poor procedure and that other variables produced changes in the freezing point value ranging from 0.001 to 0.011°C. It is recommended that specific directions be instituted for the thermistor cryoscopic method, 15.040–15.041, and that the method be subjected to a collaborative study.

Micro-Determination of p-Hydroxybenzoic Esters in Pharmaceuticals and Cosmetics

1977 ◽  
Vol 60 (1) ◽  
pp. 56-59 ◽  
Author(s):  
Emil Weisenberg ◽  
Baruch Gershon ◽  
Judith Schoenberg
Keyword(s):  
Liquid Chromatography ◽  
Phosphate Ester ◽  
Specific Method ◽  
Gas Liquid Chromatography ◽  
Direct Extraction ◽  
Flame Photometric Detector ◽  
Phenol Derivatives ◽  
Margin Of Error ◽  
Diethyl Phosphate

Abstract A rapid and specific method is described for the determination of microamounts of methyl, propyl, and butyl p-hydroxybenzoic esters (parabens) in pharmaceuticals and cosmetics. The method involves the direct extraction of parabens into benzene or chloroform followed by derivatization with phosphorochloridate. The diethyl phosphate ester derivatives are cleaned up on a Florisil minicolumn and finally measured by gas-liquid chromatography on 5% OV-210 on Gas-Chrom Q. A flame photometric detector or a KCl thermionic detector may be used. The concentration response was linear up to 40 ng parabens. The reproducibility and margin of error were tested with fortified samples. This method may be applied to the estimation of other phenol derivatives.

scholarly journals Determination of the Sensitivity of a RapidEscherichia coli O157:H7 Assay for Testing 375-Gram Composite Samples

2000 ◽  
Vol 66 (9) ◽  
pp. 4149-4151 ◽  
Author(s):  
Wan-Ling Tsai ◽  
Cynthia E. Miller ◽  
Edward R. Richter
Keyword(s):  
Escherichia Coli ◽  
Sample Size ◽  
Ground Beef ◽  
Immunomagnetic Separation ◽  
Enzyme Linked Immunosorbent Assay ◽  
Elisa System ◽  
Composite Ground ◽  
Single Size ◽  
Composite Samples

ABSTRACT Both 25-g single-size ground beef samples and 375-g composite ground beef samples were tested by a method combining an immunomagnetic separation (IMS) technique with a sandwich enzyme-linked immunosorbent assay (ELISA) system (IMS-ELISA). The results demonstrated that IMS-ELISA could detect the target, Escherichia coliO157:H7, at the level of 10−1 CFU/g of sample in either the 25- or 375-g sample size.

scholarly journals Determination of Sample Size for Speed Measurement on Urban Arterials

2016 ◽  
Vol 17 ◽  
pp. 384-390 ◽  
Author(s):  
V. Varsha ◽  
Gaurav H. Pandey ◽  
K. Ramachandra Rao ◽  
B.K. Bindhu
Keyword(s):  
Sample Size ◽  
Speed Measurement ◽  
Urban Arterials
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