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SEMI E89-1104 E © SEMI 1999, 2004 1 SEMI E89-1104 E GUIDE FOR MEASUREMENT SYSTEM ANALYSIS (MSA) This guide was technically approved b y the Global Me trics Committee and is th e direct responsibility of the North America…

SEMI E85-0705 © SEMI 1999, 2005 27

Facial

Datum

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Side rail

Bottom rail

FOUP

Bilateral Datum Plane

Horizontal Datum Plane

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A FOUP Example of Using Two Kinematic Coupling Pins, Part 2

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SEMI E89-1104

GUIDE FOR MEASUREMENT SYSTEM ANALYSIS (MSA)

This guide was technically approved by the Global Metrics Committee and is the direct responsibility of the

North American Metrics Committee. Current edition approved by the North American Regional Standards

Committee on August 16, 2004. Initially available at www.semi.org September 2004; to be published

November 2004. Originally published September 1999.

This standard was editorially modified in October 2004 to correct an editorial error in the title.

NOTICE: This document was completely rewritten in

2004.

1 Purpose

1.1 The purpose of this guide is to provide a consistent

set of terminology and describe a simplified, but

constructive, experimental approach to planning and

performing a measurement system analysis (MSA).

1.2 The goal of an MSA is to characterize the

performance capability of the measurement system

(MS) as it is intended to be used in a manufacturing or

laboratory setting.

1.2.1 Accurately identifying the MS bias and the size

and nature of all sources of variability allows one to

determine whether the MS is capable of performing its

intended function. Moreover, a well-designed MSA

can be used to identify and quantify areas that need the

most improvement.

2 Scope

2.1 This guide covers procedures for determining

specific measures of MS capability including:

 measurement variability (i.e., reproducibility)

under a variety of conditions, including

 effects of repeatability,

 load-unload, and

 time, and

 bias, including bias-related

 linearity,

 stability, and

 matching tolerance.

2.2 This guide also covers secondary metrics such as

precision-over-tolerance (P/T) ratio and signal-to-noise

ratio (SNR).

2.3 The primary focus of this guide is on determining

measurement capability of automated wafer MSs under

normal operating conditions, but the definitions and

methodologies are extendible to many other measure-

ment situations involving automated measurements on

units such as processed dice, packaged devices, flat

panel displays, piece parts, etc.

2.4 While there is no universally accepted correct way

to conduct an MSA, the approach described in this

document is supported in the technical literature (see

Section 11) and congruent with practices advocated in

ISO 5725-2. The procedures given in this guide

represent an approach to the conduct of an MSA and

provide basic reference methods that should serve for a

variety of applications. Other methods may be

appropriate in certain circumstances.

2.5 The procedures in this guide that are intended to

separate the various sources of nonsystematic (i.e.,

random) errors are based on the use of factorial

experiments and analysis of variance (ANOVA).

Because the primary focus of this guide is on evaluation

of automated MSs, the variability introduced by

different operators is expected to be minimal.

NOTE 1: Information on measurement uncertainty

calculations is provided in Related Information 1. Information

on testing measurement distributions for normality and equal

repeatability is provided in Related Information 2.

NOTICE: This standard does not purport to address

safety issues, if any, associated with its use. It is the

responsibility of the users of this standard to establish

appropriate safety and health practices and determine

the applicability of regulatory or other limitations prior

to use.

3 Limitations

3.1 Determination of MS capability is meaningless

unless the MS is in control. Methodology for

establishing and maintaining MS control is beyond the

scope of this guide. Such methodology should be a part

of a quality management system, such as that mandated

by ISO 9000 or similar standards. Additional guidance

for laboratories without established procedures may be

found in the ASTM Manual on Presentation of Data

and Control Chart Analysis.

1 Manual on Presentation of Data and Control Chart Analysis, 6th

edition, MNL 7 (ASTM International, West Conshohocken, PA,

1991)

SEMI E89-1104

3.2 This guide does not address those aspects of

measurement uncertainty associated with change in the

object being measured, either spatially or temporally.

3.3 This guide does not address determination of

measurement capability in the case of destructive

measurements on samples, or when the MS alters the

object being measured as a result of making the

measurement.

3.4 This guide does not apply to inter-laboratory

experiments designed to measure inter-laboratory

precision of test methods.

4 Referenced Standards

4.1 ISO Standards

ANSI/ISO Z540-2 — Guide to the Expression of

Uncertainty in Measurement

ANSI/ISO/ASQC A3534-1 — Statistics – Vocabulary

and Symbols – Part 1: Probability and General

Statistical Terms.

ISO 3534-3 — Statistics – Vocabulary and Symbols –

Part 3: Design of Experiments.

ISO 5725-2 — Accuracy (trueness and precision) of

measurement methods and results – Part 2: Basic

method for the determination of repeatability and

reproducibility of a standard measurement method.

ISO 9000 — Quality management systems –

Fundamentals and vocabulary, 2000.

NOTICE: Unless otherwise indicated, all documents

cited shall be the latest published versions.

5 Terminology

5.1 Terminology in this section that is not directly used

in this guide, is likely to be encountered while

conducting an MSA.

5.2 Definitions of many other terms related to

metrology and statistics can be found in VIM,

ANSI/ISO/ASQC A3534-1, and ISO 3534-3.

5.3 Definitions

5.3.1 accuracy — closeness of agreement between a

test result or the mean of a group of test results made on

an object and its true value.

2 ISO Central Secretariat, 1, rue de Varembé, Case postale 56, CH-

1211 Genève 20, Switzerland, web site:

www.iso.ch; ISO standards

are available in the United States through the American National

Standards Institute, web site:

www.ansi.org, and in most other

countries through the ISO member body.

3 International Vocabulary of Basic and General Terms in

Metrology, Second Edition [VIM] (ISO, Genève, 1993).

5.3.1.1 Discussion — Accuracy depends on both the

precision and bias of the measurement process. Since

random components of error (resulting in imprecision)

and systematic components of error (resulting in bias)

cannot be completely separated in routine use, the

reported accuracy must be interpreted as a combination

of these two elements.

5.3.2 bias — difference between the population mean

of the test results from a measurement process and the

true (accepted reference) value of the property being

measured.

5.3.2.1 Discussion — Bias is a systematic component

of measurement uncertainty. One or more systematic

error components may contribute to the bias. The true

value and the population mean are both unknown. The

true value may be estimated with the use of a consensus

value. If sufficient measurements are made to

adequately mitigate the effects of measurement

variability, the population mean may be estimated from

the sample mean





(1)

where:

x = sample mean,

n = number of measurements, and

= i

measurement value.

5.3.3 calibration — set of operations that establish the

relationship between values of quantities indicated by a

measurement system (MS) and the corresponding

values assigned to reference materials.

5.3.3.1 Discussion — The purpose of calibration is to

reduce or eliminate bias in the MS.

5.3.4 certified reference material (CRM) — reference

material, one or more of whose property values are

certified by a technically valid procedure, accompanied

by or traceable to a certificate or other documentation

issued by a certifying body.

5.3.5 coefficient of variation (CV) — population

standard deviation expressed as a percentage of the

mean value.

5.3.5.1 Discussion — CV can be estimated from the

sample standard deviation, s, and the sample mean,

,x of a distribution as follows:

100

CV (2)

CV is an appropriate measure of variability only when

the sample standard deviation is proportional to the

mean; otherwise it varies with the value of the

measurand. If the sample standard deviation is