AFM-Based Observation and Robotic Nano-manipulation (eBook)

Preface 5
Acknowledgements 7
Contents 8
1 Introduction 12
Abstract 12
1.1 Introduction of Nanotechnology 12
1.1.1 Development and Application of Nanotechnology 13
1.1.2 Characteristics of Nanotechnology 18
1.1.3 The Key Nanotechnology: Nano-observation and Manipulation 19
1.2 Primary Nano-observation Methods 20
1.2.1 Optical Microscopic Observation 20
1.2.2 SEM/TEM Based Observation 20
1.2.3 STM Based Observation 22
1.2.4 AFM Based Observation 23
1.3 Primary Nano-manipulation Methods 28
1.3.1 Self-assembly Based Nano-manipulation 28
1.3.2 Optical Tweezer Based Nano-manipulation 28
1.3.3 DEP Based Nano-manipulation 30
1.3.4 SEM Based Nano-manipulation 31
1.3.5 AFM Based Nano-manipulation 32
1.4 Application Characteristics and Problems of AFM Based Nano-manipulation 34
References 37
2 AFM Based Robotic Nano-manipulation 43
Abstract 43
2.1 AFM Introduction 43
2.1.1 Analysis of AFM Atomic Force-Distance Curve 44
2.1.2 Three Work Modes of AFM 45
2.2 AFM Based Robotic Nano-manipulation 46
2.2.1 Static Image Based Offline Nano-manipulation 46
2.2.2 Augmented Reality Based Robotic Nano-manipulation 48
2.2.3 Local Scan Based Nano-manipulation Using Landmark Observation 50
2.3 Stochastic Approach for AFM Based Robotic Nano-manipulation 52
2.3.1 Precision Analysis of AFM Tip Driver 53
2.3.2 Real-Time Tip Localization Analysis in Task Space 53
2.3.3 AFM Based Nano-manipulation Using Virtual Nano-hand 55
References 56
3 AFM Image Reconstruction Using Compensation Model of Thermal Drift 58
Abstract 58
3.1 Reconstruction Theory of AFM Thermal-Drift Image 58
3.1.1 Newton Iteration Method 59
3.1.2 Image Interpolation Method 60
3.1.2.1 Application of Image Interpolation 61
3.1.2.2 Nearest Neighbor Interpolation Method 64
3.1.2.3 Bilinear Interpolation Method 64
3.1.2.4 Newton Interpolation Method 65
3.1.2.5 Bi-cubic and B-spline Interpolation 66
3.1.3 Thermal Drift Correction Method for Scanning Image 68
3.2 Reconstruction Method for Thermal Drift Image 70
3.2.1 Compensation Model for Thermal Drift 70
3.2.2 Thermal Drift Offset Vector 72
3.2.2.1 First-Order Offset Vector 73
3.2.2.2 Second-Order Offset Vector 74
3.2.3 Offset Vector Calculation 75
3.2.3.1 Calculation of Offset Vectors in Characteristic Regions 76
3.2.3.2 Calculation of Offset Vectors in Non-characteristic Regions 77
3.2.4 Integral Image Reconstruction 79
3.3 Simulation and Experimental Analysis 81
3.3.1 Simulation and Analysis of Thermal Drift Image 81
3.3.2 Experiment and Analysis of Reconstruction of Thermal Drift Image 82
3.3.2.1 Morphological Changes of Nanoparticles 83
3.3.2.2 Verification of Thermal Drift Velocity 87
3.3.2.3 Experimental Results of Global Reconstruction 88
References 90
4 AFM Image Reconstruction Algorithm Based on Tip Model 91
Abstract 91
4.1 Theoretical Basis of AFM Tip Blind Modeling Reconstruction 91
4.1.1 Basic Concepts of Mathematical Morphology 91
4.1.2 Mathematical Description of Tip Imaging Process 94
4.1.3 Tip Morphology Estimation Algorithm 96
4.2 A Method for Improving the Speed of Tip Modeling Calculation 100
4.2.1 Pre-estimation of Tip Morphology 100
4.2.2 Improvement of Algorithm Core 103
4.3 Method for Improving the Accuracy of Tip Modeling 107
4.3.1 Definition of Denoising Threshold 107
4.3.2 Estimation of Denoising Threshold 109
4.4 The Experiment of AFM Image Reconstruction 110
4.4.1 Tip Topography Estimation 110
4.4.2 Scanning Image Reconstruction of Carbon Nano-tubes and Nano-particles 112
References 114
5 Stochastic Approach Based AFM Tip Localization 115
Abstract 115
5.1 Research of AFM Tip Localization 115
5.1.1 Stochastic Approach Based AFM Tip Localization Strategy 115
5.1.2 Nano-manipulation Coordinate System Defined on AFM 119
5.2 Analysis of Landmark Observation Model Based on Kalman Filter 121
5.2.1 Landmark Definition 121
5.2.2 Analysis of Landmark Observation 122
5.2.3 Analysis of Horizontal Observation of Landmark 123
5.2.4 Optimal Estimation of Tip Position Based on Kalman Filter 125
5.3 Establishment of Tip Motion Model 129
5.3.1 PI Based Motion Model 129
5.3.2 Creep Model of PZT 130
5.3.3 System Thermal Drift Model 131
5.4 Simulation Experiment of Tip Localization Based on Landmark Observation 133
References 135
6 Path Planning of Nano-Robot Using Probability Distribution Region 136
Abstract 136
6.1 Path Planning for Landmark Observation Using Probability Distribution Region 137
6.2 Tip Path Planning in Task Space 143
6.2.1 Basic Path Planning in Single Landmark Environment 143
6.2.2 Path Planning in Multi-landmark Environment 148
6.3 Simulation and Experimental Verification 150
6.3.1 Path Planning Based on Dijkstra Method 150
6.3.2 Path Planning Based on Ant Colony Algorithm 151
6.4 Landmark Dynamic Configuration 155
6.4.1 Definition of Landmark Domain 157
6.4.2 Virtual Nano-hand Method 159
6.4.3 Nano-manipulation Simulation Based on Virtual Nano-hand 160
References 162
7 AFM-Based Nano-manipulation Platform 163
Abstract 163
7.1 Hardware and Software Implementation of System 163
7.1.1 Hardware Platform 164
7.1.2 Software Implementation 165
7.2 AFM Tip Localization 167
7.2.1 Framework of Tip Localization System 167
7.2.2 Model Parameters Calibration and Experimental Verification 168
7.2.3 Accuracy Improvement of Tip Localization 180
7.3 AFM Nano-manipulation 184
7.3.1 Virtual Nano-hand Nano-manipulation 184
7.3.2 Demonstration of AFM Nano-manipulation 186
Index 188