This paper proposes a novel algorithm in pixel-based pattern recognition in design layout which offers simplicity, speed and accuracy to recognize any patterns that later can be used to detect problematic pattern in lithography process so they can be removed or improved earlier in design stage.:Abstract 1
Content 3
List of Figure 6
List of Tables 8
List of Abbreviations 9
Chapter 1: Introduction 10
1.1 Motivation 10
1.2 Related Work 11
1.3 Purpose and Research Question 12
1.4 Approach and Methodology 12
1.5 Scope and Limitation 12
1.6 Target group 13
1.7 Outline 13
Chapter 2: Theoretical Background 14
2.1 Problematic Pattern in Computational Lithography 14
2.2 Optical Proximity Effect 16
2.3 Taxonomy of Pattern Recognition 17
2.3.1 Feature Generation 18
2.3.2 Classifier Model 19
2.3.3 System evaluation 20
2.4 Feature Selection Technique 20
2.4.1 Wrapper-Based Methods 21
2.4.2 Average-Based Methods 22
2.4.3 Binary Geometrical Transformation 24
2.4.3.1 Image Interpolation 24
2.4.3.2 Geometric Transformation 26
2.4.3.2.1 Forward Mapping: 26
2.4.3.2.2 Inverse Mapping: 27
2.4.3.3 Thresholding 27
2.5 Machine Learning Algorithm 28
2.5.1 Linear Classifier 29
2.5.2 Linear Discriminant Analysis (LDA) 30
2.5.3 Maximum likelihood 30
2.6 Scoring (Metrics to Measure Classifier Model Quality) 31
2.6.1 Accuracy 32
2.6.2 Sensitivity 32
2.6.3 Specifity 32
2.6.4 Precision 32
Chapter 3: Method 33
3.1 Problem Formulation 33
3.1.1 T2T Pattern 35
3.1.2 Iso-Dense Pattern 36
3.1.3 Hypothetical Hotspot Pattern 37
3.2 Classification System 38
3.2.1 Wrapper and Average-based 38
3.2.2 Binary Geometric Transformation Based 39
3.3 Window-Based Raster Scan 40
3.3.1 Scanning algorithm 40
3.4 Classifier Design 42
3.4.1 Training Phase 43
3.4.2 Discriminant Coefficient Function 44
3.4.3 SigmaDi 45
3.4.4 Maximum Posterior Probability 45
3.4.5 Classifier Model Block 46
3.5 Weka 3.8 47
3.6 Average-based Influence 49
3.7 BGT Based Model 50
Chapter 4: Results 55
4.1 Wrapper and Average-based LDA classifier 55
4.2 BGT Based LDA with SigmaDi Classifier 56
4.3 Estimation Output 57
4.4 Probability Function 58
Chapter 5: Conclusion 59
5.1 Conclusions 59
5.2 Future Research 60
Bibliography 61
Selbstständigkeitserklärung 63
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:86703 |
| Date | 13 September 2023 |
| Creators | Treska, Fergo |
| Contributors | Horstmann, John Thomas, Sherazi, Syed Muhammad Yasser, Technische Universität Chemnitz |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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