Six Sigma for Electronics Design and Manufacturing

Six Sigma for
Electronics Design
and Manufacturing
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Six Sigma for
Electronics Design
and Manufacturing
Sammy G. Shina
University of Massachusetts, Lowell
McGraw-Hill
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DOI: 10.1036/0071409556
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Contents
Illustrations and Tables xvii
Abbreviations xxiii
Preface xxvi
Chapter 1. The Nature of Six Sigma and Its Connectivity 1
to Other Quality Tools
1.1 Historical Perspective 1
1.2 Why Six Sigma? 4
1.3 Defending Six Sigma 7
1.4 The Definitions of Six Sigma 8
1.5 Increasing the Cp Level to Reach Six Sigma 9
1.6 Definitions of Major Quality Tools and How 10
They Effect Six Sigma
1.7 Mandatory Quality Tools 10
1.8 Quality Function Deployment (QFD) 11
1.8.1 Engineering 11
1.8.2 Management 11
1.8.3 Marketing 12
1.9 Design for Manufacture (DFM) 00
1.10 Design of Experiments (DoE) 00
1.11 Other Quality Tools 20
1.11.1 Process mapping 21
1.11.2 Failure modes and effects analysis (FMEA) 26
1.12 Gauge Repeatability and Reproducibility (GR&R) 29
1.13 Conclusions 30
1.14 References and Bibliography 31
Chapter 2. The Elements of Six Sigma and 33
Their Determination
2.1 The Quality Measurement Techniques: SQC,
Six Sigma, Cp and Cpk 34

2.1.1 The Statistical quality control (SQC) methods 34
2.1.2 The relationship of control charts and 35
six sigma
2.1.3 The process capability index (Cp) 36
2.1.4 Six sigma approach 39
vii
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2.1.5 Six sigma and the 1.5 ␴ shift 41
2.2 The Cpk Approach Versus Six Sigma 42
2.2.1 Cpk and process average shift 43
2.2.2 Negative Cpk 44
2.2.3 Choosing six sigma or Cpk 45
2.2.4 Setting the process capability index 46
2.3 Calculating Defects Using Normal Distribution 47
2.3.1 Relationship between z and Cpk 54
2.3.2 Example defect calculations and Cpk 54
2.3.3 Attribute processes and reject analysis for 57
six sigma
2.4 Are Manufacturing Processes and Supply Parts 59
Always Normally Distributed?
2.4.1 Quick visual check for normality 59
2.4.2 Checking for normality using chi-square tests 60
2.4.3 Example of ␹
2
goodness of fit to normal 62
distribution test
2.4.4 Transformation data into normal distributions 63
2.4.5 The use of statistical software for 65
normality analysis

2.5 Conclusions 65
2.6 References and Bibliography 66
Chapter 3. Six Sigma and the Manufacturing Control Systems 69
3.1 Manufacturing Variability Measurement and Control 70
3.2 The Control of Variable Processes and Its 72
Relationship with Six Sigma
3.2.1. Variable control chart limits 74
3.2.2 Control chart limits calculations 74
3.2.3 Control and specifications limits 75
3.2.4 X
ෆ
, R variable control chart calculations 76
example
3.2.5 Alternate methods for calculating control 78
limits
3.2.6 Control chart guidelines, out-of-control 78
conditions, and corrective action
procedures and examples
3.2.7 Examples of variable control chart 82
calculations and their relationship to
six sigma
3.3 Attribute charts and their Relationship with 84
Six Sigma
viii Contents
3.3.1 The binomial distribution 85
3.3.2 Examples of using the binomial distribution 86
3.3.3 The Poisson distribution 86
3.3.4 Examples of using the Poisson distribution 87
3.3.5 Attribute control charts limit calculations 88
3.3.6 Examples of attribute control charts 89

calculations and their relationship to
six sigma
3.3.7 Use of control charts in factories that are 91
approaching six sigma
3.4 Using TQM Techniques to Maintain Six Sigma 91
Quality in Manufacturing
3.4.1 TQM tools definitions and examples 92
3.5 Conclusions 99
3.6 References and Bibliography 99
Chapter 4. The Use of Six Sigma in Determining the 101
Manufacturing Yield and Test Strategy
4.1 Determining Units of Defects 102
4.2 Determining Manufacturing Yield on a Single 104
Operation or a Part with Multiple Similar Operations
4.2.1 Example of calculating yield in a part with 105
multiple operations
4.2.2 Determining assembly yield and PCB and 106
product test levels in electronic products
4.2.3 PCB yield example 107
4.3 Determining Design or Manufacturing Yield on 108
Multiple Parts with Multiple Manufacturing
Operations or Design Specifications
4.3.1 Determining first-time yield at the electronic 110
product turn-on level
4.3.2 Example of yield calculations at the PCB 110
assembly level
4.3.3 DPMO methods for standardizing defect 112
measurements
4.3.4 DPMO charts 113
4.3.5 Critique of DMPO methods 115

4.3.6 The use of implied Cpk in product and 116
assembly line manufacturing and
planning activities
4.3.7 Example and discussion of implied Cpk in 118
IC assembly line defect projections
4.4 Determining Overall Product Testing Strategy 120
Contents ix
4.4.1 PCB test strategy 121
4.4.2 PCB test strategy example 123
4.4.3 In-circuit test effectiveness 127
4.4.4 Factors affecting test operation parameters 128
4.4.5 Test coverage 128
4.4.6 Bad and good test effectiveness 129
4.4.7 Future trends in testing 130
4.5 Conclusions 130
4.6 References and Bibliography 131
Chapter 5. The Use of Six Sigma With High- and 133
Low-Volume Products and Processes
5.1 Process Average and Standard Deviation 134
Calculations for Samples and Populations
5.1.1 Examples of the use of the t-distribution for 137
sample and population averages
5.1.2 Other statistical tools: Point and interval 138
estimation
5.1.3 Examples of point estimation of the average 139
5.1.4 Confidence interval estimation for the average 140
5.1.5 Standard deviation for samples and 142
populations
5.1.6 Examples of population variance 144
determination

5.2 Determining Process Capability 145
5.2.1 Process capability for large-volume 146
production
5.2.2 Determination of standard deviation ␴ for 148
process capability
5.2.3 Example of methods of calculating ␴ 149
5.2.4 Process capability for low-volume production 150
5.2.5 Moving range (MR) methodologies for low 150
volume: MR control charts
5.2.6 Process capability studies in industry 152
5.3 Determining Gauge Capability 154
5.3.1 GR&R methodology 156
5.3.2 Examples of GR&R calculations 158
5.3.3 GR&R results interpretation 159
5.3.4 GR&R examples 160
5.4 Determining Short- and Long-Term Process 164
Capability
x Contents
5.4.1 Process capability for prototype and early 165
production parts
5.4.2 Corrective action for process capability 168
problems
5.5 Conclusions 168
5.6 References and Bibliography 168
Chapter 6. Six Sigma Quality and Manufacturing Costs of 169
Electronics Products
6.1 The Overall Electronic Product Life Cycle Cost Model 170
6.1.1 The use of the quality and cost model to 173
achieve world-class cost and quality
6.1.2 Developing the background information cost 174

estimating of electronic products
6.1.3 Determination of costs and tracking tools 176
for electronic products
6.2 The Quality and Cost Relationship 177
6.2.1 The quality loss function (QLF) 178
6.2.2 Quality loss function example 179
6.2.3 A practical quality and cost approach 181
6.3 Electronic Products Cost Estimating Systems 182
6.3.1 Relating quality data to manufacturing six 184
sigma or Cpk levels
6.3.2 Printed circuit board (PCB) fabrication 185
technologies
6.3.3 Printed circuit board (PCB) design, 187
fabrication cost, and quality issues
6.3.4 PCB fabrication cost and quality alternative 191
example
6.4 PCB Assembly Cost Estimating Systems 192
6.4.1 Material-based PCB assembly cost system 193
6.4.2 The technology cost driver system 193
6.4.3 PCB assembly cost modifiers 197
6.4.4 Quality-based product cost models 201
6.5 Conclusions 203
6.6 References and Bibliography 203
Chapter 7. Six Sigma and Design of Experiments (DoE) 205
7.1 DoE Definitions and Expectations 206
7.1.1 DoE objectives and expectations 209
7.2 Design of Experiments (DoE) Techniques 210
Contents xi
7.2.1 Steps in conducting a successful DoE 211
experiment

7.2.2 Types of DoE experiments using 215
orthogonal arrays
7.2.3 Two-level orthogonal arrays 217
7.2.4 Three-level orthogonal arrays 220
7.2.5 Interaction and linear graphs 221
7.2.6 Multilevel arrangements and combination 225
designs
7.2.7 The Taguchi contribution to DoE 227
7.3 The DoE Analysis Tool Set 227
7.3.1 Orthogonal array L9 saturated design 228
example: Bonding process optimization
7.3.2 Graphical analysis conclusions 231
7.3.3 Analysis of DoE data with interactions: 232
Electrical hipot test L8 partial factorial
Resolution IV example
7.3.4 Statistical analysis of DoEs 234
7.3.5 Statistical analysis of the hipot experiment 236
7.4 Variability Reduction Using DoE 238
7.5 Using DoE Methods in Six Sigma Design and 240
Manufacturing Projects
7.6 Conclusions 241
7.7 References and Bibliography 241
Chapter 8. Six Sigma and Its Use in the Analysis of Design 243
and Manufacturing for Current and New
Products and Processes
8.1 Current Product Six Sigma Strategy 244
8.1.1 Process improvement in current products 246
8.2 Transitioning New Product Development to 250
Six Sigma
8.2.1 Design analysis for six sigma 251

8.2.2 Measuring the capability of current 253
manufacturing processes
8.2.3 Investigating more capable processes for 255
new products
8.2.4 Case studies of process capability 256
investigations for manufacturing: Stencil
technology for DoE
8.3 Determining Six Sigma Quality in Different Design 260
Disciplines
8.3.1 Mechanical product design process 260
xii Contents
8.3.2 Mechanical design and tolerance analysis 261
8.3.3 Types of tolerance analysis 262
8.3.4 Statistical tolerance analysis for mechanical 263
design
8.3.5 Tolerance analysis example 263
8.3.6 Statistical analysis of the mechanical design 265
example
8.3.7 Tolerance analysis and CAD 266
8.3.8 Tolerance analysis and manufacturing 266
processes
8.3.9 Mechanical design case study 267
8.3.10 Thermal design six sigma assessment 268
example
8.3.11 Six sigma for electronic circuits with 270
multiple specifications
8.3.12 Special considerations in Cpk for design of 271
electronic products
8.3.13. The use of design quality analysis in systems 272
architecture

8.4 Applying Six Sigma Quality for New Product 272
Introduction
8.4.1 Optimizing new manufacturing processes 273
8.4.2 New process optimization example: 274
Target value manipulations and variability
reduction DoE
8.4.3 Trade-offs in new product design disciplines 277
8.4.4 New product design trade-off example— 277
Screening DoE followed by in-depth DoE for
defect elimination in thermal printer design
8.4.5 New product test strategy 283
8.4.6 New product test strategy example 283
8.5 Conclusions 284
Chapter 9. Six Sigma and the New Product Life Cycle 287
9.1 Background: Concurrent Engineering Successes 288
and New Trends
9.1.1 Changes to the product realization process 291
9.2 Supply Chain Development 294
9.2.1 Outsourcing issues 296
9.2.2 Dependency versus competency 297
9.2.3 Outsourcing strategy 298
Contents xiii
9.2.4 Supply chain communications and 300
information control
9.2.5 Quality and supply chain management 303
9.2.6 Supply chain selection process 305
9.3 Product Life Cycle and the Six Sigma Design 307
Quality Issues
9.3.1 Changes in electronic product design 309
9.3.2 Changing traditional design communications 310

and supplier involvement
9.3.3 Design process communications needs 313
9.4 Conclusions 314
9.5 References and Bibliography 315
Chapter 10. New Product and Systems Project Management 317
Using Six Sigma Quality
10.1 The Quality System Review and Quality-Based 318
Project Management Methodologies
10.1.1 The quality-based system design process 318
10.1.2 Six sigma quality-based system design 319
process benefits
10.1.3 Historical perspective of project 320
management
10.1.4 Project management of the product 323
development process
10.2 Technical Design Information Flow and Six Sigma 327
System Design
10.2.1 Opportunities in six sigma for system or 328
product design improvements
10.2.2 The system design process 329
10.2.3 The system design steps 329
10.2.4 Composite Cpk 330
10.2.5 Selecting key characteristics for systems 332
design analysis
10.2.6 Standardized procedures in design to 334
determine the composite Cpk
10.2.7 Standardized procedures in manufacturing 335
to determine the composite Cpk
10.3 Conclusions 338
Chapter 11. Implementing Six Sigma in Electronics Design 339

and Manufacturing
11.1 Six Sigma Design Project Management Models 340
xiv Contents
11.1.1 Axioms for creating six sigma within 340
the organization
11.2 Cultural Issues with the Six Sigma Based System 348
Design Process
11.3 Key Processes to Enhance the Concurrent 350
Product Creation Process
11.3.1 Six sigma phased review process 351
11.3.2 Six sigma quality advocacy and the 353
quality systems review
11.3.3 Six sigma manufacturability assessment 353
and tactical plans in production
11.4 Tools to Support Suggested Processes 355
Index 357
Contents xv
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Illustrations and Tables
Illustrations
Figure 1.1 World-class benchmarks percentage improvements per year. 5
Figure 1.2 QFD product planning matrix. 13
Figure 1.3 Raychem CATV new connector QFD matrix. 14
Figure 1.4 SMT process QFD matrix. 16
Figure 1.5 Use of a DFM scoring system. 18
Figure 1.6 Structured analysis (SA) components. 22
Figure 1.7 Process mapping example. 24
Figure 1.8 Failure mode and effect analysis (FMEA) chart. 28
Figure 2.1 Conceptual view of control charts. 35
Figure 2.2 Specification and tolerance of a typical product. 36

Figure 2.3 Intersection of process capability and specification 37
limits to determine the defect level.
Figure 2.4 Conceptual view of control and capability concepts. 38
Figure 2.5 Normal distribution with mean shifted by 2.5 ␴.40
Figure 2.6 Specification and control limits. 42
Figure 2.7 Cp and Cpk sample calculations. 44
Figure 2.8 Graphical presentation of normal distribution. 48
Figure 2.9 Graphical presentation of normal distribution with parts 53
compliance percentage and multiple ␴ limits.
Figure 2.10 z transformation. 55
Figure 2.11 Negative and positive z transformation. 56
Figure 2.12 Quick visual check for normality in Example 2.4.1. 61
Figure 2.13 Normal plot of for data set in Example 2.4.1. 64
Figure 2.14 Plot of observed (dark) versus expected (clear) frequencies. 64
Figure 2.15 Plot of Example 2.4 data set original (top) and transformed 65
by –log ͙x
ෆ
on the bottom.
Figure 3.1 Types of control charts. 71
Figure 3.2 X
ෆ
Control chart example. 79
Figure 3.3 R control chart example. 80
Figure 3.4 Bonding process control chart example. 81
Figure 3.5 Surface cleanliness control chart example. 94
Figure 3.6 Shipment integrity cause and effect diagram. 96
Figure 3.7 Control chart flow diagram. 96
Figure 3.8 Pareto diagram—% reasons for production downtime 97
Figure 4.1 First-time yield (FTY) IC wire bonding example. 106
Figure 4.2 An example of a multistep manufacturing process line. 108

xvii
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Figure 4.3 DPMO chart example. 115
Figure 4.4 IC assembly line Cpk example. 118
Figure 4.5 PCB test alternatives. 121
Figure 5.1 t distribution with standard normal distribution. 135
Figure 5.2 t distribution with significance ␣. 166
Figure 5.3 Confidence interval around the mean ␮ and ␴ is known. 141
Figure 5.4 ␹
2
distribution with significance ␣. 143
Figure 5.5 Obtaining confidence limits from ␹
2
distribution with 144
confidence (1 – ␣)%.
Figure 5.6 Sources of process variation and error. 155
Figure 5.7 Accuracy and precision target example. 156
Figure 5.8 Summation of averages and standard deviations. 157
Figure 5.9 Distributions of prototype and early production of parts. 166
Figure 6.1 Product life cycle stages. 171
Figure 6.2 Typical cost distribution of an electronic product. 176
Figure 6.3 Cost history of an electronic product based on the 177
concept stage.
Figure 6.4 Volume sensitivity of the cost of an electronic product. 178
Figure 6.5 Electronic design implementation in PCBs. 183
Figure 6.6 PCB fabrication steps. 185
Figure 6.7 A typical approach to printed circuit board (PCB) assembly. 194
Figure 7.1 Basic elements of DoE. 206
Figure 7.2 Possible effects of different factors. 209
Figure 7.3 The use of an L8 as full factorial versus saturated design. 219

Figure 7.4 The plot of interactions of the example in Table 7.6. 222
Figure 7.5 Linear graphs for the interactions of L8 shown in Table 7.7. 223
Figure 7.6 Bonding process DoE graphical analysis. 231
Figure 7.7 Hipot design DoE graphical analysis. 234
Figure 7.8 Visualizing the error of the hipot experiment. 238
Figure 8.1 Progression of quality tools for existing products. 245
Figure 8.2 Cause and effect diagram for mixed technology PCBs. 248
Figure 8.3 Graphical analysis of DoE for mixed technology soldering of 248
PCBs.
Figure 8.4 Histogram of solder defects distribution 6 months before 250
and after DoE.
Figure 8.5 Overall new product quality, including design and 251
manufacturing.
Figure 8.6 Design six sigma example—bandpass filter. 252
Figure 8.7 Tolerance analysis example, three square parts. 264
Figure 8.8 Mechanical design of a typical vibrating angioplasty probe. 267
Figure 8.9 S/N analysis for fine pitch SMT processing variability. 276
Figure 8.10 Printer qaulity DoE test pattern. 278
Figure 8.11 Prodcut test strategy. 284
Figure 9.1 Concurrent engineering culture. 288
Figure 9.2 Life cycle models for different products. 292
Figure 9.3 Traditional versus concurrent engineering project 294
communications.
Figure 9.4 Core competencies chart and outsource matrix. 299
xviii Illustrations and Tables
Figure 9.5 Supplier management models. 302
Figure 9.6 Mature sales volume for personal computer family. 308
Figure 10.1 Typical electronic product development cycle. 322
Figure 10.2 Development project time line: phases and milestones. 326
Figure 10.3 Project communications monthly meeting example. 327

Figure 10.4 Cpk tree. 331
Figure 11.1 Spider web diagram of six sigma project goals. 351
Tables
Table 1.1 Criteria rating (CR) to select a solder system for PCB assembly 12
Table 1.2 HP 34401A multimeter DFM results 19
Table 2.1 Defect rates in PPM for different quality levels and 41
distribution shifts
Table 2.2 Cpk and process average shift 44
Table 2.3 Standard normal distribution 49
Table 2.4 Examples of calculating defect rates, Cp, and Cpk 56
Table 2.5 ␹
2
goodness of fit test using case study 63
Table 3.1 Control chart factors 75
Table 3.2 Control chart limit calculations example 77
Table 3.3 Probabilities for out-of-control conditions 82
Table 3.4 TQM tool usage 92
Table 4.1 Yield calculation in a three-step production line 109
Table 4.2 Yield calculation in a line with n parts in a three-step 110
production line
Table 4.3 DPMO grouping of defects and opportunities for PCB 112
assemblies
Table 4.4 DPMO chart data 114
Table 4.5 PCB test methods comparison 123
Table 4.6 PCB test methods scenario 1 (two strategies) 124
Table 4.7 PCB test methods scenario 2 (four sigma company) 125
Table 4.8 PCB test methods scenario 3 (six sigma company), 126
three strategies
Table 4.9 Factors that affect test effectiveness 128
Table 5.1 Selected values of t

␣,␯
of student’s t distribution 137
Table 5.2 Error of the t
␣,v
of student’s t distribution 139
Table 5.3 Selected values of ␹
2
distribution 143
Table 5.4 Amount of data required for process capability studies 146
Table 5.5 Example of process capability studies for PCB assembly line 154
Table 5.6 R
ෆ
estimator of ␴ for GR&R 158
Table 5.7 GR&R example 162
Table 6.1 Product development life cycle stages attributes 172
Table 6.2 Complexity-based process DPUs from a typical PCB 189
fabrication shop
Table 6.3 Design-related causes of PCB defects 191
Table 6.4 Classifications for different types of PCB assemblies 193
Table 6.5 Material-based cost model, NRE and test costs 195
Illustrations and Tables xix
Table 6.6 Cost rate calculations for machine-loaded TH components 197
Table 6.7 Technology cost model with modifiers for PCB assembly 198
Table 6.8 Cost model drivers example for sheet metal fabrication 200
Table 6.9 PCB quality-based technology cost models 201
Table 7.1 “One factor at a time” experiments 216
Table 7.2 XOR logic table for interaction level determinations 216
Table 7.3 L8 orthogonal array 217
Table 7.4 L16 orthogonal array 220
Table 7.5 L9 orthogonal array 221

Table 7.6 Interaction example using L4 orthogonal array 221
Table 7.7 Interaction scenarios for L8 with Resolution IV design 223
Table 7.8 Interaction scenarios for L16 with confounding 224
Table 7.9 Plackett and Burman L12 orthogonal array 225
Table 7.10 L18 orthogonal array 225
Table 7.11 Multilevel designs with L8 orthogonal arrays 226
Table 7.12 Bonding process DoE 230
Table 7.13 Hipot DoE experiment 233
Table 7.14 F table value for 95% confidence or 0.05 confidence 236
Table 7.15 Hipot design ANOVA statistical analysis 237
Table 7.16 Hipot design ANOVA statistical analysis with pooled error 237
Table 8.1 Design and analysis of DoE for mixed technology PCBs 247
Table 8.2 Specification for bandpass filter example 252
Table 8.3 Simulation results for Cpk analysis of a bandpass filter 253
Table 8.4 Quality data for PCB assembly manufacturing processes 254
Table 8.5 Quality analysis of a two-sided PCB with TH, SMT, and 255
mechanical assembly and multiple components and leads
Table 8.6 Quality drivers for printed circuit board (PCB) assembly 256
Table 8.7 DoE stencil technology experiment factor and level selection 258
Table 8.8 Stencil technology DoE L16 design 259
Table 8.9 Stencil technology percent contribution analysis of average 259
solder deposition area
Table 8.10 Stencil technology quality loss function (QLF) formula 260
Table 8.11 Tolerance analysis for three-part example, worst-case 264
analysis
Table 8.12 Tolerance analysis for three-part example, six sigma analysis. 265
Case 3: statistical tolerance
Table 8.13 Statistical design analysis of angioplasty probe 268
Table 8.14 Thermal design six sigma assessment 268
Table 8.15 Composite Cpk design analysis of an RF amplifier 270

Table 8.16 Fine pitch SMT processing parameters DoE 275
Table 8.17 Defect classifications for printer DoE 278
Table 8.18 Printer quality screening DoE L8 design 279
Table 8.19 Printer quality screening DoE defect results 280
Table 8.20 Printer quality screening DoE results analysis 280
Table 8.21 Printer quality second DoE design 281
Table 8.22 Printer quality screening DoE defect results 282
Table 8.23 Printer in-depth DoE analysis and final recommnedations 282
Table 9.1 Status of companies outsourcing hardware design and 290
manufacturing capabilities
xx Illustrations and Tables
Illustrations and Tables xxi
Table 9.2 Common issues in selecting outsourced products and 296
competencies
Table 9.3 Supply chain communications 301
Table 9.4 Weighted criteria for supplier selection matrix 306
Table 9.5 Weighted quality criteria for supplier selection matrix 306
Table 9.6 Comparison of PCB assembly costs 307
Table 9.7 Attributes and metrics of success for each design phase 311
Table 9.8 Changes from traditional engineering to new methodologies 314
Table 9.9 Communications summary for design phases 315
Table 10-1 Total product development process concept-to-development 322
criteria
Table 10.2 Cpk design quality matrix selection for systems 334
specifications and modes
Table 10.3 Example of a machining center Cpk status 337
Table 11.1 Factors that affect test effectiveness 354
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Abbreviations
AQAP Advance product quality planning and control plan

ANOVA Analysis of variance
AV Appraiser variation
BIST Built in self-test
BOM Bill of materials
CAD Computer-aided design
CAE Computer-aided engineering
CAM Computer-aided manufacturing
CEM Contract electronic manufacturers
CLT Central limit theory
CIM Computer-integrated manufacturing
CPI Continuous process improvement
Cp Capability of the process
Cpk Capability of the process, with average shift
CR Criteria rating
DA Decision analysis
DFD Data flow diagrams
DFM Design for manufacture
DFT Design for testability
DoE Design of experiments
DOF Degrees of freedom
DPMO Defect per million opportunities
DPU Defects per unit
ECO Engineering change orders
ERP Enterprise requirements planning
ESI Early supplier involvement
EV Equipment variation
IPC Institute for Interconnecting and Packaging of Electronic Circuits
FMEA Failure mode effect analysis
FT Functional test
FTY First-time yield

GMP Good manufacturing practices
GR&R Gauge repeatability and reproducibility
Hipot High potential
IC Integrated circuit
ICT In-circuit test
JIT Just in time
MR Moving range
MTBF Mean time between failure
NIH Not invented here
xxiii
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xxiv Abbreviations
NS Normal (probability) score
NTF No trouble found
OA Orthogonal arrays
OEM Original equipment manufacturers
PCB Printed circuit board
PPM Parts per million
PTF Polymer thick film
QA Quality assurance
QFD Quality function deployment
QLF Quality loss function
RFI Radio frequency interference
ROI Return on investment
RPN Risk priority number
RSS Root sum of the squares
SA Structure analysis
SMT Surface mount technology
SOW Same old way
SL Specification limits

SS Sum of the squares
TH Through-hole (technology)
TQC Total quality control
TQM Total quality management