Data mining and diagnosing IC fails / Leendert M. Huisman.

Format:

Book

Author/Creator:

Huisman, Leendert M.

Series:

Frontiers in electronic testing

Language:

English

Subjects (All):

Integrated circuits--Testing--Statistical methods.

Integrated circuits.

Semiconductors--Failures.

Semiconductors.

Data mining.

Integrated circuits--Testing.

Statistics.

Physical Description:

270 pages : illustrations ; 24 cm.

Place of Publication:

New York : Springer, 2005.

Summary:

Data Mining and Diagnosing IC Fails addresses the problem of obtaining maximum information from (functional) Integrated Circuit fail data about the defects that caused the fails. It starts at the highest level from mere sort codes, and drills down via various data mining techniques to detailed logic diagnosis. The various approaches discussed in this book have a thorough theoretical underpinning, but are geared towards applications on real life fail data and state of the art ICs. This book brings together a large number of analysis techniques that are suitable for IC fail data, but that are not available elsewhere in a single place. Several of the techniques, in fact, have been presented only recently in technical conferences.

Data Mining and Diagnosing IC Fails begins with a discussion of sort codes and yield analysis. It then discusses various data mining techniques centered on fail syndrome commonalities and the statistics of embedded object fails. It gives a thorough discussion of the area dependence of the yield and of the recognition of spatial patterns of failing die or embedded objects. Next, it gives a detailed analysis of the relationship between defect coverage and yield. It ends with a description of state of the art logic diagnosis techniques.

The purpose of the book is to bring together in one place a large number of analysis, data mining and diagnosis techniques that have proven to be useful in analyzing IC fails. The descriptions of the techniques and analysis routines is sufficiently detailed that profession manufacturing engineers can implement them in their own work environment. There are many techniques for analyzing IC fails, but they are scattered over the professional IC test and diagnosis literature, and in various statistics and data mining handbooks. Moreover, many data mining techniques that are standard in other data analysis environments, and that are appropriate for analyzing IC fails, have not yet been employed for that purpose. There is a clear need for a single source for all these analysis techniques, suitable for professional IC manufacturing and test engineers.

Contents:

2 Statistics 29

1 Statistical distributions 29

1.1 Binomial and multinomial distributions 29

1.2 Poisson and compound Poisson distributions 31

1.3 Negative binomial distribution 32

2 Likelihood 33

2.1 Maximum likelihood 34

2.2 Likelihood ratio 35

3 Bootstrapping 37

3 Yield Statistics 39

1 Yield and Defect Level 40

1.1 Final yield 40

1.2 Defect Level 40

2 Example: experimental wafer yields 42

3 Test fallout 44

3.1 First fail probabilities 44

3.2 Statistical distribution of fails 46

4 Measuring First fail probabilities 47

4.1 Fallout histories 47

4.2 Maximum likelihood estimation 48

5 Comparing wafers 50

4 Area Dependence of the Yield 55

1 General Model 58

1.1 Primitive Polluters 58

1.2 Yield and Moments 60

1.4 General Properties 62

2 Center-Satellite Model 65

2.1 Center-Satellite Yield 66

2.2 Center-Satellite Moments 67

2.3 Numerical results 68

3 Estimating the Area Dependence 71

3.1 Comparing different products 72

3.2 Quadrat method 74

3.2.1 Problems with the quadrat method 74

3.2.2 Numerical technique 75

3.2.3 Numerical results 77

5 Statistics of Embedded Object Fails 83

1 General definitions 85

2 Correlations and clustering 86

3 Example of embedded object fails 87

4 Object and cell fail probabilities 87

4.1 Estimating cell fail probabilities 88

4.2 Comparing different models 91

4.3 Small fail probabilities 91

4.4 Example of cell fail probabilities 92

5 Partial data collection 93

6 Sampling defective devices 95

7 Fail probabilities of object components 98

7.1 Component fail estimates 99

7.2 Special cases 99

6 Fail Commonalities 101

1 Commonality measures 102

1.1 Pairwise commonality 103

1.2 Commonality of sets of signatures 105

2 Embedded objects 105

3 Logic fails 106

3.1 Signatures based on fail data only 107

3.2 Signatures based on backtracing 109

3.3 Signatures based on cells 112

3.4 Signatures based on diagnosis 112

4 Clustering 113

7 Spatial Patterns 117

1 Non-random patterns 118

1.1 Clustering parameter 119

1.2 Geometric properties of the pattern 119

1.2.1 Geometric centers 119

1.2.2 SLOR 121

2 Classifying patterns 123

2.1 Marginal probabilities 124

2.2 Experimental results 125

2.3 Clustering patterns 130

8 Test Coverage and Test Fallout 133

1 Yield and coverage 133

1.1 Defect model 134

1.1.1 Poisson and negative binomial models 135

1.1.2 Compound model 135

1.1.3 Independent defect model 136

1.2 Coverage and yield 137

1.3 Properties of the yield curve 140

2 Observed yield curve 143

9 Logic Diagnosis 145

1 Defect model 147

2 Fault selection 149

3 Alternatives to simulation 151

4 Scoring matches 152

5 Experimental results 155

6 Resolution of logic diagnosis 158

7 Using passing patterns 161

10 Slat Based Diagnosis 165

2 Logic defect model 167

2.1 Physical justification 167

2.2 Logic defects 168

2.3 SLAT patterns 170

3 SLAT based diagnosis 171

3.1 Initial Diagnosis 172

3.2 Comparison with stuck-at fault diagnosis 175

3.3 Potential accuracy risks 176

3.4 Non SLAT patterns 177

4 Multiplet analysis and splats 179

4.1 Splat structure 180

4.1.1 Completely separated splats 181

4.1.2 General case 181

4.1.3 Complete set of multiplets 185

4.1.4 Risks 186

4.2 M incomplete 187

5 Greedy search for splats 188

6 Interpretation 190

7 Experimental Results 191

7.1 Comparison with stuck-at fault diagnosis 192

7.2 Specific diagnoses 195

11 Data Collection Requirements 197

1 Design requirements 197

2 Test requirements 201

3 Data collection requirements 202

Appendix A Distribution of IC Fails 205

1 General Definition 205

1.1 Fallout fluctuations 207

1.2 Defect Level 208

Appendix B General Yield Model 209

Appendix C Simplified Center-Satellite Model 213

Appendix D Quadrat Analysis 217

1 Estimation 217

2 General equation for the cluster coefficient 221

Appendix E Cell Fail Probabilities 223

Appendix F Characterization Group 229

1 Likelihood equations 229

2 Heterogeneous model 231

3 Homogeneous model 233

4 Validity of the likelihood estimates 234

Appendix G Component Fail Probabilities 237

1 Maximum Likelihood estimation 237

2 Location of the maxima of the likelihood function 239

Appendix H Yield and Coverage 243

1 Average yield 244

2 Variance of the yield 246

Appendix I Estimating First Fail Probabilities from the Fallout 251.

Notes:

Includes bibliographical references and index.

ISBN:

0387249931

0387263519

OCLC:

60558678

Publisher Number:

9780387249933

9780387263519