## Glossary

NOTE: This glossary is being adapted from a glossary kindly donated by Bill Heavlin of Advanced Micro Devices. At the current time, it may contain terms not used by the COST system.

Welcome to the hypertext dictionary. It contains selected terms from engineering statistics. Current topic areas include experimental design, metrology, and computer experiments.

# [A] [B] [C] [D] [E] [F] [G] [H] [I] [J] [K] [L] [M] [N] [O] [P] [Q] [R] [S] [T] [U] [V] [W] [X] [Y] [Z]

## [A]

 accuracy In metrology, the total measurement variation, including not only precision (reproducibility), but also the systematic offset between the average of measured values and the true value. inspection The measurement of a characteristic and its comparison to a standard. additive effect A property of a model describing a physical process whereby the average or expected change from changing a particular input factor does not depend upon the values of other input factors. An additive effect has no associated interactions. analysis of variance A way of presenting the calculations for the significance of a particular factor's effect, especially for data in which the influence of several factors is being considered simultaneously. Analysis of variance decomposes the sum of squared residuals from the mean into non-negative components attributable to each factor, or combination of factor interactions. Usually it is useful to distinguish between fixed and random effects. In the case of only random effects, the term variance components is often preferred. assignable cause A synonym for special cause. audit The periodic observation of performed activities to verify compliance with documented requirements. Return to the Top of the Page

## [B]

 bar chart A graph that reports several values by drawing a bar from zero to each value. Each bar is suitably labeled. Critics of good graphic design distinguish further between vertical bar charts and horizontal bar charts. Vertical bar charts, sometimes also called "column charts," plot the values against the y-axis and the labels along the x-axis; they are recommended for reporting data in time order. Horizontal bar charts plot the values along the x-axis and the labels along the y-axis; they are recommended for reporting data that is not chronological. Note that for horizontal bar charts, the labels are naturally oriented horizontally, and more readable than for vertical bar charts. Finally, note that recommended practice is to order the axes meaningfully, either using a natural sequence in the labels, or by ranking the values plotted. See also Pareto charts. bias The difference between the average or expected value of a distribution, and the true value. In metrology, the difference between precision and accuracy is that measures of precision are not affected by bias, whereas accuracy measures degrade as bias increases. binomial distribution An important theoretical distribution used to model discrete events, especially the count of defectives. The binomial distribution depends on two parameters, n and p. n is the total number of trials; for each trial, the chance of observing the event of interest is p, and of not observing it, 1-p. The binomial distribution assumes each trial's outcome is independent of that of any other trial, and models the sum of events observed. Unlike the Poisson distribution, the binomial distribution sets a maximum number of events n, the sample size that can be observed. Unlike the hyergeometric distribution, the binomial distribution assumes the events it counts are independent. blocking The practice of partitioning an experiment into subgroups, each of which is restricted in size, time, and/or space. Good experimental design practice has all factors changing within blocks, unit assignment within blocks randomized, and block order and assignment randomized. Box-Benkhen experiment An experimental design with three levels on each factor that allows the estimation of a full quadratic model, including interactions. Box-Benkhen designs have two parts: centerpoints, and points lying on one sphere, equally distant from the centerpoint. The latter points consists of small two-level full factorials where some factors are fixed at their center values. The number of centerpoints is chosen to establish rotatability. Compare to central composite designs. box plot A univariate graphical display of a distribution designed to facilitate the comparison of several groups, especially when each group has a substantial number of observations. Each group is represented by a box; the ends of the box denote the 25th and 75th percentiles; a mid-line denotes the median. In addition, from the ends of the box outward are two lines drawn to either (a) the largest and smallest values of the distribution, or (b) the largest and smallest values that are not considered outliers. By the latter convention, individual values that are considered outliers are plotted as particular points. Some software plots the average value also. brushing The technique of highlighting a subgroup of observations, especially in a scatterplot matrix, but sometimes also in a histogram or other graphical display. In a scatterplot matrix, brushing helps is visualizing multivariate data. Typical computer implementations allow the user to redefine the subgroup in real time.

## [C]

 calibration In metrology, the process or method for comparing actual readings to their known values, and also of making suitable adjustments so that the agreement between the two is improved. constraint In either an experiment or for a production process, a limitation in the range of a factor or combination of factors that is either physically not possible or greatly undesirable to execute. capability The natural variation of a process due to common causes. capability index, Cpk A measure of the natural variation of a stable process compared to the closeness of the specification limit(s). When the process is both stable and normally distributed, it is possible to estimate from Cpk the fraction of product out of specification. Let LSL denote the lower specification limit and let USL denote the upper specification limit. Let AVG denote the mean or similar typical value of a distribution, and let SIGMA denote an estimate of the total common cause variation. Then Cpk is defined as the smaller of [ AVG - LSL ]/3*SIGMA and [ USL - AVG ]/3*SIGMA. Sometimes only a lower or only an upper specification is appropriate. For a lower limit, the one-sided capability index called Cpl, defined as [ AVG - LSL ]/3*SIGMA, can used instead; for an upper limit, Cpu, defined as [ USL - AVG ]/3*SIGMA. Because of their similarity, Cpk is sometimes used as a general term to include the cases of both one- and two-sided specifications. capability index, Cpk with 25 percent precision and 95 percent confidence If the process is repeated with at least 33 distinct repetitions, Cp, defined as [USL-LSL]/6*SIGMA, has a 95 percent confidence interval that is about plus or minus 25 percent of the estimated Cp value. This same principle holds approximately for the confidence interval of Cpk. Further detail is available in AMD technical report 320. Alternative methods are available to achieve comparable precision with smaller sample sizes under some circumstances. See the AMD technical report 326. capability study Any study of the common cause variability of a process. capable process a process in which there is sufficient tolerance in the specification range that, in principle, one can detect out-of-control situations and effect corrective action without placing production material in jeopardy A process for which the capability index Cpk exceeds 1.0. (Other criteria for Cpk are sometimes promoted. Among these are 1.33, 1.5, and 2.0, but these latter values are usually reserved for the label "manufacturable." centerpoint In an experiment with quantitative factors, the experimental condition corresponding to all factors being set to the mid-point between their high and low values. Centerpoints serve to test for the presence of curvature, and give information about quadratic effects. When repeated, centerpoints also provide estimates of the magnitude of the experimental error. central composite design Also known as a Box-Wilson or star composite design. An experimental design of three parts: A two-level full or fractional factorial design; "Star" or axial points in which each factor is varied to high and low levels with all other factors held constant; centerpoints. The configuration of star points leads to variations: If one codes the two-level design part with -1 and +1, then the original Box-Wilson proposal varied the star points at particular values larger than 1; the precise value was chosen to ensure rotatability. Another alternative is to restrict the star points to +/-1. Central composite designs have a further appeal in that they are amenable to iterative experiments and blocking. Compare to Box-Benkhen designs. characteristic A distinguishing feature of a process or its output on which variables or attributes data can be collected. The response of a process. characterization Any description of a process or its measurable output that aids in the prediction of its performance. common cause A source of natural variation that affects all of the individual values of the process output being studied. Typically, common causes are numerous, individually contribute little to the total variation (although the total variation can still be substantial), and are difficult to eliminate. computer experiment A study of a fundamental physical process by the use of one or more computer simulators. Like empirical experiments, input variables (factors) are systematically changed to assess their impact upon simulator outputs (responses). Unlike empirical experiments, the simulator responses are deterministic, and this has implications: Computer experiments can appropriately have their factors with intermediate levels and the scope, especially the number of runs, can be more ambitious. Further, modeling methods based on interpolators (especially kriging) emerge as a viable approach. Good practice is to use Latin hypercubes for computer experiments, and advanced nonparametric modeling methods such as kriging, neural networks, and multivariate adaptive regression splines (MARS) in the data analysis stage. Important applications of computer experimental methods are for determining process optima and for evaluating process tolerances. confidence interval Any statement that an unknown parameter is between two values with a certain probability. For example, if one says that the 95 percent confidence interval for theta is 1.1 to 10.3, this corresponds to the probability statement that Pr{ 1.1 <= theta <= 10.3 }=0.95. Based on the observation of a certain set of data, the range of plausible values of an unknown parameter that are consistent with observing that data. For example, if one says the 95 percent confidence interval for theta is 1.1 to 10.3 then this is equivalent to saying that based on the data observed, there is a 95 percent chance that theta is between 1.1 and 10.3 correlation Correlation is a measure of the strength of the (usually linear) relationship between two variables. The usual correlation coefficient, called the Pearson correlation coefficient, ranges from -1 to 1. A value of +1 corresponds to the case where the two variables are related perfectly by an increasing relationship; a value of -1 corresponds to a perfect, but decreasing relationship. In the case of the Pearson correlation coefficient, a value of +1 (-1) implies the relationship is linear and increasing (decreasing). critical parameters A critical parameter is a measurable characteristic of a material, process, equipment, measurement instrument, facility, or product that is directly or indirectly related to the fitness for use of a product, process, or service. cross-validation A family of methods based on the idea that the most unbiased test of the predictive error is by applying it to data that was not used in the building of the initial predictive model. A common application is to partition a dataset into two parts, to fit the model on the first part, and to assess the predictive capability of that model on the second part. customers Organizations that use the products, information, or services of an operation.

## [D]

 data reduction The process of calculating from several numbers one or fewer numbers. An example is that one might have 9 readings taken across a wafer. A common data reduction would be to use the average and standard deviation, which is only two numbers. The benefits of data reduction are usually simplicity, interpretation ease, greater focus on issues of interest, and small data files. deterministic The property of being perfectly repeatable, and without experimental or observational error. Usually achievable only in computer experiments. distribution A representation of the frequency of occurrence of values of a variable, especially of a response. dot plot A form of a histogram for which an observation with a value within a certain range is plotted as a dot a fixed interval above the previous dot in that same range. Useful for small numbers of observations.

## [E]

 effect The change in the average or expected value of a given response due to the change of a given factor. The change of the given factor is usually from the lowest to the highest value of those tried experimentally, and the units of the effect are usually in the same units as the response. EVOP, Evolutionary Operation An abbreviation for "evolutionary operation". An EVOP is a special type of on-line experiment with several distinguishing features: The experimental material is production material intended to be delivered to customers. In each experimental cycle, the standard production recipe is changed. The experimental factor levels are less extreme than in conventional off-line experiments. The experiment is run over a longer term with more material than in conventional off-line experiments. Return to the Top of the Page

## [F]

 factor The input variable of a process, and especially of an experiment. Experimental factors are particularly those variables that are deliberately manipulated during the experiment. Experimental factors can be divided further into control factors and noise factors. Control factors are those factors that are easy to control, and usually have a strong influence on the response. (A classic example is the time involved for a deposition process.) Noise factors are factors that are either difficult or inconvenient to control. A difficult-to-control noise factor might be the ambient air flow around a furnace tube. An inconvenient-to-control noise factor might be the recent use history of a wet clean sink. factor level In experimental design, the value that an input variable or factor takes on. factor range In experimental design, and especially for a quantitative factor, the difference between the highest value that the factor takes on and the lowest. factorial experiment An experiment in which the values of each factor are used in combination with all the values of all other factors. A fractional factorial experiment takes a judicious subset of all combinations, with the following objectives in mind: the total number of experiments is small, the experimental space is well covered, for subsets of factors (say of size 2, 3, or 4), the total number of experimental combinations is kept large. Return to the Top of the Page

## [G]

 Gaussian distribution See normal distribution. goodness-of-fit Any measure of how close a probability model reproduces the frequencies of an observed distribution. A measure, such as R-squared, of how close a statistical model predicts observed values.

## [H]

 histogram A graphical display of a statistical distribution; a form of bar chart. One axis (usually x) is the scale of the values observed, the second (usually y) is the frequency that observations occur with (approximately) that value. hypergeometric distribution An important distribution used to model discrete events, especially the count of defectives when sampling without replacement. The hypergeometric distribution depends on three parameters, N, n and D. N is the known and finite population size, n the known sample size (constrained to be less than or equal to N), and D, the unknown number of defectives. Unlike the binomial distribution, the hypergeometric distribution assumes sampling is without replacement, and that its parameters are all integer-valued.

## [I]

 imputation The replacement of unknown, unmeasured, or missing data with a particular value. The simplest form of imputation is to replace all missing values with the average of that variable. More sophisticated imputation methods use the correlation structure among observed variables. Imputation is most common in surveys of human populations. It is also used in certain computer experiment applications. interaction A property of a physical process (of a model describing such a process) wherein the average (or predicted average) change in the response from changing a particular input factor depends on the values of other input factors. interpolator Any predictive algorithm that always perfectly reproduces the observations used for model construction. Useful for computer experiments.

## [K]

 kriging An interpolator easily generalized to multiple dimensions and arbitrary configurations of observed points. Nonetheless, kriging is analogous to least squares. A point at which a kriging prediction is desired is thought to be more "correlated" to the closer observed points in the observation space. Further, as this point approaches another that is actually observed, the correlation approaches 1.0. From these ideas, one can formalize a prediction method, kriging. For an experiment of n observations, kriging requires the inversion of an n x n matrix, making it awkward to use to large n.

## [L]

 lack of fit A property of a model with respect to a set of observations. Lack of fit refers to the degree to which the model does not predict or fit the observations. Lack of fit can be due to experimental error and uncertainty in the process obtaining the observations, or it may be due to a defect in the model. Latin hypercube design An experimental design consisting of n trials, and for which each factor has n distinct levels. Usually the factor levels are equally spaced. The best Latin hypercube designs are based on orthogonal arrays. Latin hypercube designs are especially useful for computer experiments. Latin hypercube sampling A computer experimental method that uses Latin hypercube designs in order to estimate distributions of the simulator outputs. The use of Latin hypercube designs allows Latin hypercube sampling to be quite a bit more precise than Monte Carlo methods. The distributions of the input factors are represented in the spacing of the factor levels. LDL, lower detection limit The level at which a measurement system ceases to discriminate effectively between background standard deviations. logistic function The function 1/(1+exp(-x)). The logistic function is skew-symmetric about zero, since logistic(x)=0.5-logistic(-x). Applications include modeling dose-response curves, heavy-tailed distributions, and as a "squashing" function in neural network modeling.

## [M]

 MCA Measurement capability assessment, or sometimes a measurement capability analysis. A metrology characterization. Sematech definitions focus on (a) repeatability and (b) reproducibility. Broader definitions would assess (c) sensitivity to changes in the phenomenon being measured--such sensitivity is desirable--and (d) sensitivity to features other than the phenomenon being measured--such sensitivity is not desirable. measurement error the variability observed that can be attributed to the metrology or measurement system. Measurement error can be decomposed further into miscalibration, in sensitivity, repeatability, and reproducibility. metrology study Sometimes called a gauge capability study, or measurement capability assessment. Such a study quantifies the capabilities and limitations of a measurement instrument, often estimating its repeatability, reproducibility, and sometimes its sensitivity. mixture experiment An experimental design in which each experimental run is constrained such that when summed across the factors, the factor levels are constrained to sum to a constant. The typical applications involve chemical experiments in which the factors are liquids, or sometimes gases. In such a case, it is the proportion of each liquid ingredient, not its weight or volume, that is the essential issue. model A mathematical statement of the relation(s) among variables. Models can be of two basic types, or have two basic parts: statistical models, which predict a measured quantity; probability models, which predict the relative frequency of different random outcomes. Monte Carlo sampling A computer experimental method that uses random numbers in order to estimate distributions of simulator outputs.

## [N]

 neural nets See neural network models. neural network models A highly flexible modeling method that postulates one or more layers of unobserved variables. Each unobserved variable is a linear function of variables of the previous layer (and the first layer are the factors, or model inputs). As output to the next layer, the output of each unobserved variable is nearly always transformed by a nonlinear function, most commonly the logistic function. Neural networks are sometimes use for analysis of computer experiments, especially when the size of the experiment makes kriging impractical. noise factor Especially in an experiment, a factor or process input that can be either difficult or inconvenient to control. Noise factors also include product use conditions (the temperature, test conditions, environment). Usually distinguished from control factors. noise-to-signal ratio The ratio of the measurement system's precision to the average measurement value; the reciprocal of the signal-to-noise ratio. The noise-to-signal ratio allows one to express the magnitude of measurement precision on a percentage scale. normal distribution A symmetric distribution with one high point or mode, sometimes also called the bell curve. The average is one of many statistical calculations that, even for only a moderate amount of data, tend to have a distribution of that resemble the normal curve. In industry, there are four important properties of the normal distribution: it is symmetric, within plus and minus one standard deviation about 68 percent of the distribution is enclosed, within plus and minus two standard deviations, 95 percent, and within plus and minus three standard deviations, 99.7 percent.

## [O]

 objective methods Methods of data collection, and especially of data analysis, characterized by the fact that they do not depend on the opinions or knowledge particular to an individual. Objective methods are reproducible, in a scientific sense, and in principle amenable to reduction to software algorithms. optimal design The approach to creating experimental designs using a computer algorithm maximizing an objective funtion. The most common objective function is the determinant of the coefficients' variance-covariance matrix; such designs are called D-optimum. In contrast to the optimal design approach is that based on orthogonal arrays. optimum Especially as determined by an experiment, the combination of factor setpoints that achieve the best balance of the responses of interest. The average response values achieved at such a set of factor setpoints. orthogonal array A table consisting of rows and columns with the property that for any pair of columns (factors) all combinations of values (levels) occur, and further, all combinations occur the same number of times. outliers Observations whose value is so extreme that they appear not to be consistent with the rest of the dataset. In a process monitor, outliers indicate that assignable or special causes are present. The deletion of a particular outlier from a data analysis is easiest to justify when such an usual cause has been identified.

## [P]

 Pareto analysis A technique for problem solving in which all potential problem areas or sources of variation are ranked according to their contribution. Poisson distribution An important theoretical distribution used to model discrete events, especially the count of defects in an area. The Poisson distribution depends on one parameter, lambda, which represents the average defect density per observation area (or volume, time interval, etc.). The Poisson distribution assumes that the counts of defects in two non-overlapping observation units are independent. Further, the Poisson distribution assumes the distribution of defect counts depend only on the area in which they are to be observed. Unlike the binomial distribution, the Poisson distribution in principle sets no limit to the number of defects that can be observed in any area. Of particular interest the semi-conductor industry, the Poisson probability of observing zero defects in a region of area A, exp{-lambda A}, is useful for yield modeling. population The entire set of potential observations (wafers, people, etc) about whose properties we would like to learn. As opposed to sample. precision in metrology, the variability of a measurement process around its average value. Precision is usually distinguished from accuracy, the variability of a measurement process around the true value. Precision, in turn, can be decomposed further into short term variation or repeatability, and long term variation, or reproducibility. A fuzzy concept term for the general notion that one knows more or has shorter confidence intervals if one has more data; that is, more data gives greater precision in answers and decisions. probability plot A plot designed to assess whether an observed distribution has a shape consistent with a theoretical distribution, especially with the normal distribution. The values observed are plotted against the expected order statistics from the theoretical distribution. When a straight line is apparent, the observed and theoretical distributions are said to have the same shape. Probability plots are especially good when the observed distribution consists of many observations, and useful for comparing at most only a few groups. process A combination of people, procedures, machinery, material, measurement equipment, and environmental conditions for specific work activities. A repeatable sequence of activities with measurable inputs and outputs. process signature The characterization of a process, including its sensitivity to input variables, its magnitude of natural variation, its sensitivity to variation in incoming material, and its dynamic and output profiles, both when operating naturally and when behaving aberrantly. process capability study A study that quantifies the common cause variability of a process. See also capability study.

## [R]

 randomization, scientific The assignment of experimental material to treatments and treatment order through the use of random number tables. The selection of observational units through the use of random number tables. Scientific randomization is to be distinguished from arbitrary assignments and selection, and from systematic assignments (e.g. wafers 1-12 receive treatment A, 13-24 treatment B). range For a given set of observations, the difference between the highest and lowest values. repeatability In metrology, the component of measurement precision that is the variability in the short term, and that occurs under highly controlled situations (e.g. same metrology instrument, same operator, same setup, same ambient environment, etc.). reproducibility In metrology, the total measurement precision, especially including the components of variability that occur in the long term, and occurring from one measurement instrument to another, one laboratory to another, etc. residual The difference between the actual value observed and the prediction or fitted value derived from a model. Residuals give information both about the model's lack of fit, and also about experimental error of the measurement process. resolution In experimental design, especially for two-level designs, the length of the word of the shortest confounding relationship. Geometrically, design resolution corresponds to the 1 plus the strength. In metrology, the number of significant digits of a measurement system that can be meaningfully interpreted. response The measured output of a process or experiment. Responses usually depend on the choice of metrology tool. In planning experiments, several responses are usually of interest, and their selection is tied closely to overall purpose of the study. response surface model (RSM) A polynomial model of several factors, especially one including terms for linear, quadratic, and second-order crossproducts. robust methods Methods of data analysis that are robust are not strongly affected by extreme changes to small portions of the data; their answers do not change very much from the presence of outliers. A classic example of a robust method is the median. rotatable The property of an experimental design that minimizes the correlation among the terms of a full quadratic model, (including interactions), thereby allowing one to select some terms without regard to the significance of other terms. A generalization of orthogonality to response surface designs. R-squared A statistic for a predictive model's lack of fit using the data from which the model was derived. R-squared is calculated as 1 minus the following ratio: ```SUM[ squared residuals from model ]/SUM[ squared deviations from mean ] ``` A perfectly fitting model yields an R-squared of 1. The latter definition is flawed by giving more credit to complicated models than is appropriate. To achieve an average value of zero when the model has no merit, R-squared-adjusted is often proposed.

## [S]

 sample The set of observational units (wafers, people, etc) whose properties our study is to observe. When we select a sample by scientific randomization, we are more easily able to generalize our conclusions to the population of interest. As opposed to population. For a given characteristic, the collection of measurements that are actually observed. sample size The number of observations in, or planned to be in, a study or other investigation. Key considerations in selecting a particular sample size are value associated with any particular level of precision, the costs of obtaining observations, and available resources. Some generic advice on sample sizes is 16, to estimate the center of a distribution by its average, 20, to estimate the correlation between two measurements, 32 per group, to estimate average difference between two groups, 50, to estimate the standard deviation of a distribution. sampling distribution The distribution of a summary quantity or statistic. scatter plot A graph of a pair of variables that plots the first variable along the x-axis and the second variable along the y-axis. In a scatterplot, the points of successive pairs are not connected. scatterplot matrix A graph of several variables that plots all pairs of variables in a corresponding scatterplot. In turn, these scatterplots are arranged in the form of an upper triangular matrix. In any row of this matrix, the y axes of all plots are always the same variable; in any column, the x axes also the same variable. sensitivity In metrology, the rate at which the average measurement changes to changes in the true value. Often reported in units of percentage change to unit percentage change. The term is also used in the interpretation of response surface models. special cause A source of variation that is large, intermittent or unpredictable, affecting only some of the individual values of the process output being studied. Also called an assignable cause. specification limits The numerical values defining the interval of acceptability for a particular characteristic. split A group of experimental units that is processed in identical fashion. For example, a 2x2 factorial experiment would have four splits. When applied to a lot of 24 wafers, 6 wafers would be assigned to each "split." stability The degree to which observations of a process can be represented by a single random "white noise" distribution, in which the prediction of the next value is not improved by knowing the process history. stable process A process that is in a state of statistical control. standard deviation A measure of spread or dispersion of a distribution. It estimates the square root of the average squared deviation from the distribution average, sometimes called the root-mean-square. Among all measures of dispersion, the standard deviation is the most efficient for normally distributed data. Also, unlike the range, it converges to a single value as more data from the distribution is gathered. standard error The standard deviation for a statistic's sampling distribution. Because many have sampling distributions that are approximately normal, plus and minus 2 standard errors is usually an approximate 95 percent confidence interval. statistic A value calculated from sample data. statistical design of experiments (SDE) Also called design of experiments (DoE, DoX). The theory of experimental design emphasizing factorial and fractional factorial designs, response surface modeling, and analysis of variance methods. A particular experiment based on this theory. The scientific principles, experimental design strategies, and model building and evaluation techniques that lead to the efficient and thorough characterization and/or optimization of products and processes. strength For an orthogonal array, the largest dimension t such that any subset of t factors is a full factorial design.

## [T]

 target value The ideal value of a parameter or characteristic. transformation A function that serves to modify a response or factor, usually motivated to make a particular model fit better or be more easily interpreted. The most common transformation is to replace a variable by its logarithm. 2-level designs A category of experimental designs in which the input factors take only two distinct values (two distinct levels).

## [U]

 uncertainty A term for the fuzzy concept of qualifying statement of what is known or concluded with quantitative statements of probability. Uncertainty usually has two aspects: that created by the experimental (i.e. observational) error associated with taking observations, and that implied by the use of imperfect models.

## [V]

 variance components Variance components are estimates of contributions to total common cause variation that are attributable to distinct causal or sampling parameters. One example is to describe total thickness variation as the sum of contributions from variation in gases, temperature, power, etc. Another example is to describe the total variation in an electrical parameter in terms of the sum of contributions from lot-to-lot variation, wafer-to-wafer variation, within wafer variation, and measurement error. variation The difference among individual outputs of a process. The causes of variation can be grouped into two major classes-- common causes and special causes; the common cause variation can often be decomposed into variance components.

## [Y]

 yield The number of units that pass some inspection criteria divided by the number submitted.