Yield and Reliability in Microwave Circuit and System Design

By: Michael D. Meehan, John Purviance

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Hardcover | 1 December 1993

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Hardcover
300 Pages

Dimensions(cm)
24.13 x 16.51 x 2.54

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This reference is for anyone involved with microwave design. It tackles the practical aspects of microwave statistical design and introduces statistical design techniques that encompass many different applications. This presentation focuses on two main example areas - microwave circuits and systems - but any application with a complex relation between design variables and performance and design variable uncertainty can benefit from statistical design.

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You Can Find This Book In

Non-Fiction Engineering & Technology Technology in General Technical Design Electronics & Communications Engineering Electronics Engineering Microwave Technology Computing & I.T.Graphical & Digital Media Applications Computer-Aided Design CAD Energy Technology & Engineering Electrical Engineering

Circuits & Components Science Physics Materials & States of Matter Condensed Matter Physics including Liquid State & Solid State Physics

Foreword	p. xiii
Preface	p. xv
Acknowledgments	p. xvii
Introduction	p. 1
The Economics of Yield and Reliability Design	p. 1
Background Terminology and Scope	p. 3
The Design and Development Process	p. 5
Design and Development for Manufacturability	p. 6
Testing Model	p. 7
Specifications	p. 8
Elements of the Test Model	p. 9
Failure Mechanisms	p. 10
Manufacturing Model	p. 10
Definition of High Yield	p. 13
Ways To Achieve High Yield	p. 13
Parameter Aging and Environmental Model	p. 13
Design	p. 14
Single-Point Optimization Design Approach	p. 14
Extending Single-Point Procedures With Statistical Design	p. 16
Sources of Parameter Value Uncertainty	p. 17
When To Use Statistical Circuit Design	p. 19
Voltage Divider	p. 19
Low-Frequency Operational Amplifier	p. 20
High-Frequency Amplifier	p. 21
Examples of Statistical Circuit Design	p. 21
Butterworth Filter	p. 22
A 2- to 6-GHz Feedback Amplifier	p. 23
Satellite-Receiver System	p. 23
Tunable Active Filter	p. 25
Summary of Examples	p. 25
Conclusion	p. 25
Important Ideas From Chapter 1	p. 26
Yield	p. 29
Introduction	p. 29
Two Ways To Describe Yield	p. 29
Mathematical Viewpoint: Calculating Yield	p. 30
Geometric Viewpoint: Seeing Yield	p. 30
Parameter Space and Performance Space	p. 30
Parameter Vector, P	p. 30
Parameter Space, P	p. 31
The Performance Space M, and the Measurement Vector M	p. 32
Design Specification, S	p. 33
Acceptable Performance Region, M[subscript a]	p. 34
Performance Function G(P)	p. 34
Acceptability Region in Parameter Space, P[subscript a]	p. 36
Example--A Voltage Divider	p. 37
Tolerance Region, T	p. 37
Parameter Statistics	p. 38
Random Variables	p. 39
Probability Density Function, f[subscript p](P)	p. 40
Average or Nominal Value	p. 41
Variance	p. 42
Higher-Order Moments	p. 43
Uniqueness	p. 43
Multiple Random Parameters and Their Joint PDF	p. 43
Covariance Matrix	p. 45
Independent and Uncorrelated Random Parameters	p. 45
Higher-Order Statistics	p. 47
Geometric Approach to Yield Calculation	p. 48
The General Geometric Approach	p. 49
Yield With Uniform Independent Parameters	p. 51
Example--A Voltage Divider	p. 52
Mathematical Approach to Yield Calculation and Yield as a Multidimensional Integral	p. 53
Conclusion	p. 54
Important Ideas From Chapter 2	p. 54
Calculating Yield	p. 57
Introduction	p. 57
Monte Carlo Integration	p. 59
Fundamental Theorem of Monte Carlo	p. 59
Ratio of Volumes Interpretation	p. 60
The Definite Integral of a Binary Function	p. 62
Monte Carlo Approach to Yield Calculation	p. 63
Confidence Intervals	p. 65
Variance Reduction	p. 66
Importance Sampling	p. 68
Geometric Approach to Yield Calculation	p. 70
The Parts of Yield Calculation	p. 71
Background	p. 71
n-Dimensional Geometry and Convex Sets	p. 71
The Yield-Calculation Elements	p. 72
Combining the Parts: Yield-Calculation Methods	p. 77
Regionalization	p. 77
Simplical Approximation	p. 79
Efficient Simplical Approximation	p. 80
Ellipsoidal Region Approximation	p. 84
Radial Approximation	p. 85
Polynomial Approximation With Cuts	p. 86
Dynamic Constraint Approximation	p. 90
Monte Carlo	p. 92
Conclusion	p. 94
Important Ideas From Chapter 3	p. 95
Statistical Sensitivity	p. 101
Introduction	p. 101
Classic Sensitivity	p. 102
Interpretation of Sensitivity	p. 102
Sensitivity in Optimization	p. 103
Manufacturing Sensitivity	p. 103
Three Sensitivity Concepts	p. 104
Illustrative Problems	p. 105
Review of Sensitivity Studies	p. 108
Single-Point Sensitivity	p. 109
Multiparameter Sensitivity	p. 110
Large-Change Sensitivity	p. 111
Multiparameter Large-Change Sensitivity	p. 112
Performance Variance Reduction, Taguchi Methods	p. 113
Manufacturing Sensitivity	p. 117
Performance Statistical Sensitivity	p. 117
Yield Statistical Sensitivity	p. 122
Performance Variance Sensitivity	p. 124
Performance Variance Factor	p. 125
Statistical Sensitivity Calculation	p. 126
Performance and Yield Factor	p. 127
Average Performance and Yield Sensitivity Calculation	p. 128
Statistical Sensitivity Management and Reduction	p. 129
Sensitivity Management	p. 129
Sensitivity Reduction	p. 131
Examples	p. 132
Lug Nut	p. 132
Salen and Key Filter	p. 134
Conclusion	p. 137
Important Ideas from Chapter 4	p. 139
Yield Optimization	p. 143
Introduction	p. 143
The Optimization Problem	p. 144
Classification of Optimization Methods and Goals	p. 146
Single-Point (Nominal) Performance Optimization	p. 147
Objective (Error) Function Formulation	p. 147
Gradient Methods	p. 149
Direct-Search Methods	p. 151
Brinkmanship Design	p. 155
Statistical Optimization	p. 156
Design Centering	p. 156
Statistical-Optimization Error Function	p. 160
Yield-Optimization Approaches	p. 160
Deterministic Methods for Yield Optimization	p. 161
Simplical Approximation	p. 161
Multicircuit	p. 163
Sampling-Based Methods for Yield Optimization	p. 164
Statistical Exploration	p. 164
Parametric Sampling	p. 166
Radial Exploration	p. 168
Yield Factor Histograms	p. 169
Sensitivity Reduction	p. 175
Conclusion	p. 175
Important Ideas From Chapter 5	p. 176
Statistical Modeling and Validation	p. 179
Introduction	p. 179
Survey of Statistical Modeling	p. 181
Elements of Statistical Modeling	p. 185
Statistical Characterization	p. 187
Verification	p. 188
Tests for Multivariate Statistical Equivalence	p. 188
The Generalized Kolmogorov-Smirnov Test	p. 188
Nearest Neighbor Test	p. 189
Multivariate Verification of FET Data	p. 189
Summary of Multivariate Statistical Verification	p. 191
Statistical-Model Development and Extraction	p. 192
Design Scenario Using the "Average" Device	p. 192
Moments	p. 193
Graphical Methods for Statistical Modeling: Frequency and Cumulative Frequency Distributions	p. 194
Truth Model	p. 195
Design Centering, Yield, and the Truth Model	p. 195
Statistical-Interpolation Model	p. 197
Summary of Statistical-Model Development	p. 199
Proposed Framework for Statistical Modeling	p. 200
Step 1: Characterization	p. 201
Step 2: Deterministic-Model Error Analysis	p. 202
Step 3: Statistical-Model Development	p. 203
Step 4: Extraction and Verification	p. 203
Step 5: Database Updating	p. 203
Conclusion	p. 204
Important Ideas From Chapter 6	p. 204
Examples and Case Studies	p. 209
Introduction	p. 209
Example--A Comprehensive Design Using a Lowpass Filter	p. 209
Comments	p. 209
An Extended-Design Methodology	p. 210
The Statistical-Design Methodology in Practice	p. 211
Summation	p. 218
Example--A 2- to 6-GHz GaAs MMIC Feedback Amplifier	p. 218
Comments	p. 218
Preliminary Information	p. 218
Statistical-Parameter Model	p. 222
Statistical Optimization and Analysis	p. 222
Results	p. 223
Example--A Satellite-Communications System	p. 223
Comments	p. 223
Preliminary Information	p. 227
Statistical System-Design Methodology	p. 227
Analogue--Two Amplifiers and a Filter	p. 228
Analogue--A Satellite Receiver	p. 230
Summation	p. 230
Case Study--A 0.5- to 2.5-GHz MMIC Gain Block	p. 234
Comments	p. 234
Preliminary Information	p. 234
Amplifier Design	p. 235
Statistical-Response Prediction With the Database Model	p. 236
Summation	p. 238
Case Study--Small-Signal Yield Analysis	p. 238
Comments	p. 238
Preliminary Information	p. 238
Approach	p. 239
Sensitivity Equations and Coefficients	p. 240
Analogue--A Broadband Low-Noise MMIC Distributed Amplifier	p. 242
Summation	p. 245
Case Study--A 7- to 11-GHz Low-Noise MMIC Amplifier	p. 246
Comments	p. 246
Preliminary Information	p. 246
CAD-System Overview	p. 248
A Three-Stage 7- to 11-GHz Low-Noise Amplifier	p. 249
Summation	p. 253
Case Study--Design to Cost	p. 253
Comments	p. 253
Preliminary Information	p. 253
Design-to-Cost Framework	p. 255
Results	p. 255
Monte Carlo Confidence-Interval Tables	p. 261
Index	p. 271
Table of Contents provided by Syndetics. All Rights Reserved.

Yield and Reliability in Microwave Circuit and System Design

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

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