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

Shuai Yuan, Lianqing Liu, Zhidong Wang, Ning XI (Autoren)

eBook Download: PDF

2020 | 1st ed. 2020
XII, 184 Seiten
Springer Singapore (Verlag)
978-981-15-0508-9 (ISBN)

Infos

This book highlights the latest advances in AFM nano-manipulation research in the field of nanotechnology. There are numerous uncertainties in the AFM nano-manipulation environment, such as thermal drift, tip broadening effect, tip positioning errors and manipulation instability. This book proposes a method for estimating tip morphology using a blind modeling algorithm, which is the basis of the analysis of the influence of thermal drift on AFM scanning images, and also explains how the scanning image of AFM is reconstructed with better accuracy. Further, the book describes how the tip positioning errors caused by thermal drift and system nonlinearity can be corrected using the proposed landmark observation method, and also explores the tip path planning method in a complex environment. Lastly, it presents an AFM-based nano-manipulation platform to illustrate the effectiveness of the proposed method using theoretical research, such as tip positioning and virtual nano-hand.

Shuai Yuan, Ph.D., graduated from Shenyang Institute of Automation, Chinese Academy of Sciences, and is currently an Associate Professor at Shenyang Jianzhu University. He has presided over a number of projects, such as the National Natural Youth Science Foundation of China, National Post-doctoral Special Foundation of China, National Post-doctoral General Foundation of China, Natural Science Foundation of Liaoning Province, and Liaoning College and University basic Scientific Research Fund. He has also participated in the National High Technology Research and Development Program of China, the key project of the National Natural Science Foundation of China, and multiple projects of provincial or municipal Natural Science Funds. Prof. Yuan has published more than 50 papers in national and international scientific journals and conferences, and a textbook. He has served as a chairman for international conferences including IEEE-CYBER (2016), IEEE-Nanomedicine (2016), IEEE-CYBER (2017), and IEEE-WRC (2018), and also a reviewer for conferences and international journals such as IEEE-ROBIO and IEEE-CYBER. At present, he is investigating micro/nano manipulation, image processing and pattern recognition, and robot navigation and control.

Lianqing Liu received the B.S. degree in industry automation from Zhengzhou University, Zhengzhou, China in 2002, and the Ph.D. degree from the Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China in 2009.

He is currently serving as a Professor for the Shenyang Institute of Automation, Chinese Academy of Sciences. His current research interests include nanorobotics, intelligent control, and biosensors. Dr. Liu was awarded the Early Government/Industrial Career Award by the IEEE Robotics and Automation Society in 2011 etc.

Zhidong Wang received the B.S. degree from the Beijing University of Aeronautics and Astronautics, Beijing, China in 1987, and the M.Sc. and Ph.D. degrees in engineering from the Graduate School of Engineering, Tohoku University, Sendai, Japan in 1992 and 1995, respectively.

He is currently a Professor with the Department of Advance Robotics, Chiba Institute of Technology, Chiba, Japan. His current research interests include human-robot interaction and cooperation systems, distributed autonomous robot systems, micro/nano robotics, and application of intelligent robot technologies for the disabled.

Ning Xi received the D.Sc. degree in systems science and mathematics from Washington University in St. Louis, St. Louis, MO, USA in 1993, and the B.S. degree in electrical engineering from the Beijing University of Aeronautics and Astronautics, Beijing, China.

Currently, he is the Chair Professor of Robotics and Automation in the Department of Industrial and Manufacturing System, and the Director of Emerging Technologies Institute of the University of Hong Kong. Before joining the University of Hong Kong, he was the University Distinguished Professor, John D. Ryder Professor of Electrical and Computer Engineering and Director of Robotics and Automation Laboratory at Michigan State University in U.S. He also served as the founding head of the Department of Mechanical and Biomedical Engineering at City University of Hong Kong (2011-2013). His research interests include robotics, manufacturing automation, micro/nano manufacturing, nano sensors and devices, and intelligent control and systems. 

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 15.2.2020
• Zusatzinfo: XII, 184 p. 135 illus., 104 illus. in color.
• Sprache: englisch
• Themenwelt: Naturwissenschaften ► Physik / Astronomie ► Festkörperphysik
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Maschinenbau
• Schlagworte: image reconstruction • Nano-Manipulation • Robotic Control • Thermal-drift • Tip morphology
• ISBN-10: 981-15-0508-X / 981150508X
• ISBN-13: 978-981-15-0508-9 / 9789811505089