Nano Optoelectronic Sensors and Devices

Dr Ning Xi is Distinguished Professor of Electrical and Computer Engineering and received his D.Sc. degree in Systems Science and Mathematics from Washington University in St. Louis , Missouri in December, 1993. He received his M.S. degree in computer science from Northeastern University , Boston , Massachusetts , and B.S. degree in electrical engineering from Beijing University of Aeronautics and Astronautics. Currently, he is John D. Ryder Professor of Electrical and Computer Engineering in the Department of Electrical and Computer Engineering at Michigan State University . Dr. Xi received the Best Paper Award in IEEE/RSJ International Conference on Intelligent Robots and Systems in August, 1995. He also received the Best Paper Award in the 1998 Japan-USA Symposium on Flexible Automation. Dr. Xi was awarded the first Early Academic Career Award by the IEEE Robotics and Automation Society in May, 1999. In addition, he is also a recipient of National Science Foundation CAREER Award. His research interests include robotics, manufacturing automation, micro/nano systems, and intelligent control and systems. Dr King W.C. Lai is Assistant Professor in the Department of Mechanical and Biomedical Engineering at City University, Hong Kong. He has over 10 years of research experience in micro/nano manipulation and micro/nano assembly. His main research interests include development of micro/nano sensors using MEMS and nanotechnology; design and fabrication of MEMS/nano systems and devices, optical sensing system and photovoltaics, nanobiotechnology, automation and manipulation of micro/nano scale systems. He has contributed to the research and development of nanomanufacturing technology for various nanodevices. He developed a micro/nano robot and a microinjection system for microassembly and microspotting of nanomaterials such as carbon nanotube, graphene etc. He has also developed apply the systems for the observation and manipulation of different biological samples such as living cells and DNA strands. He has published more than 80 peer-reviewed conference papers, book chapters and high-quality journals in the field of micromanipulation, nanorobotics and MEMS devices

• PrefaceAcknowledgmentsAbout the EditorsList of ContributersChapter 1 Introduction 1.1 Overview 1.2 Impact of Nanomaterials 1.3 Challenges and Difficulties in Manufacturing Nanomaterials-Based Devices 1.3.1 Role of Microfluidics 1.3.2 Role of Robotic Nanoassembly 1.4 Summary ReferencesChapter 2 Nanomaterials Processing for Device Manufacturing 2.1 Introduction 2.2 Characteristics of Carbon Nanotubes 2.3 Classification of Carbon Nanotubes using Microfluidics 2.3.1 Dielectrophoretic Phenomenon on CNTs 2.3.2 Experimental Results: Separation of Semiconducting CNTs 2.4 Deposition of CNTs by Microrobotic Workstation 2.5 Summary ReferencesChapter 3 Design and Generation of Dielectrophoretic Forces for Manipulating Carbon Nanotubes 3.1 Overview 3.2 Dielectrophoretic Force Modeling 3.2.1 Modeling of Electrorotation for Nanomanipulation 3.2.2 Dynamic Modeling of Rotational Motion of Carbon Nanotubes for Intelligent Manufacturing of CNT-Based Devices 3.2.3 Dynamic Effect of Fluid Medium on Nano Particles by Dielectrophoresis 3.3 Theory for Microelectrode and Electric Field Design for Carbon Nanotube Applications 3.3.1 Microelectrode Design 3.3.2 Theory for Microelectrode Design 3.4 Electric Field Design 3.5 Carbon Nanotubes Application-Simulation Results 3.5.1 Dielectrophoretic Force: Simulation Results 3.5.2 Electrorotation (Torque): Simulation Results 3.5.3 Rotational Motion of Carbon Nanotubes: Simulation Results 3.6 Summary ReferencesChapter 4 Atomic Force Microscope-Based Nanorobotic System for Nanoassembly 4.1 Introduction to AFM and Nanomanipulation 4.1.1 AFM’s Basic Principle 4.1.2 Imaging Mode of AFM 4.1.3 AFM-Based Nanomanipulation 4.2 AFM-Based Augmented Reality System 4.2.1 Principle for 3D Nanoforce Feedback 4.2.2 Principle for Real-Time Visual Feedback Generation 4.2.3 Experimental Testing and Discussion 4.3 Augmented Reality System Enhanced by Local Scan 4.3.1 Local Scan Mechanism for Nanoparticle 4.3.2 Local Scan Mechanism for Nanorod 4.3.3 Nanomanipulation with Local Enhanced Augmented Reality System 4.4 CAD-Guided Automated Nanoassembly 4.5 Modeling of Nanoenvironments 4.6 Automated Manipulation of CNT 4.7 Summary ReferencesChapter 5 On-Chip Band Gap Engineering of Carbon Nanotubes 5.1 Introduction 5.2 Quantum Electron Transport Model 5.2.1 Nonequilibrium Green’s Functions 5.2.2 Poisson’s Equation and Self-Consistent Algorithm 5.3 Electrical Breakdown Controller of a CNT 5.3.1 Extended Kalman Filter for Fault Detection 5.4 Effects of CNT Breakdown 5.4.1 Current-Voltage Characteristics 5.4.2 Infrared Responses 5.5 Summary ReferencesChapter 6 Packaging Processes for Carbon Nanotube-Based Devices 6.1 Introduction 6.2 Thermal Annealing of Carbon Nanotubes 6.3 Electrical and Optical Responses of Carbon Nanotubes After Thermal Annealing 6.4 Parylene Thin Film Packaging 6.5 Electrical and Optical Stability of the CNT-Based Devices After Packaging 6.6 Summary ReferencesChapter 7 Carbon Nanotube Schottky Photodiodes 7.1 Introduction 7.2 Review of CNT Photodiodes 7.3 Design of CNT Schottky Photodiodes 7.4 Symmetric Schottky Photodiodes 7.5 Asymmetric Schottky Photodiodes 7.6 Summary ReferencesChapter 8 Carbon Nanotube Field-Effect Transistor-Based Photodetectors 8.1 Introduction 8.2 Back-Gate Au–CNT–Au Transistors 8.3 Back-Gate Ag–CNT–Ag Transistors 8.4 Back-Gate Au–CNT–Ag Transistors 8.5 Middle-Gate Transistors 8.6 Multigate Transistors 8.7 Detector Array Using CNT-Based Transistors 8.8 Summary ReferencesChapter 9 Nanoantennas on Nanowire-Based Optical Sensors 9.1 Introduction 9.2 Nanoantenna Design Consideration for IR Sensors 9.2.1 Optical Nanoantennas Combined with CNT-Based IR Sensors 9.3 Theoretical Analysis: Nanoantenna Near-Field Effect 9.4 Fabrication of Nano Sensor Combined with Nanoantenna 9.5 Photocurrent Measurement on Nano Sensor Combined with Nanoantenna 9.6 Summary ReferencesChapter 10 Design of Photonic Crystal Waveguides 10.1 Introduction 10.2 Review of the Photonic Crystal 10.3 Principle for Photonic Crystal 10.4 Phototonic Band Gap of Photonic Crystal 10.4.1 Effect from Dielectric Constants 10.4.2 Effect from Different Structures 10.5 Photonic Crystal Cavity 10.5.1 Basic Design of Photonic Crystal Defect 10.5.2 Defect from Dielectric Constants 10.5.3 Defect from Dielectric Size 10.5.4 Effect from Lattice Number 10.6 Design and Experimental Results of Photonic Crystal Cavity 10.6.1 Design 10.6.2 Photoresponses of CNT-Based IR Sensors with Photonic Crystal Cavities 10.6.3 Photocurrent Mapping of the CNT-Based IR Sensors with Photonic Crystal Cavities 10.7 Summary ReferencesChapter 11 Organic Solar Cells Enhanced by Carbon Nanotubes 11.1 Introduction 11.2 Application of Carbon Nanotubes in Organic Solar Cells 11.3 Fabrication of Carbon Nanotube-Enhanced Organic Solar Cells 11.4 Performance Analysis of OSCs Enhanced by CNTs 11.4.1 J-V of SWCNTs-Enhanced OSCs Under Illumination 11.4.2 J-V of SWCNTs-Enhanced OSCs in Dark 11.5 Electrical Role of SWCNTs in OSCs 11.6 Summary ReferencesChapter 12 Development of Optical Sensors Using Graphene 12.1 Introduction 12.2 Fabrication of Graphene-Based Devices 12.3 Dielectrophoretic Effect on Different Graphene Flakes 12.4 Electrical and Optical Behaviors of Various Graphene-Based Devices 12.5 Summary ReferencesChapter 13 Indium Antimonide (InSb) Nanowire-Based Photodetectors 13.1 Introduction 13.2 Growth of InSb Nanowires 13.3 Photodetectors Using Single InSb Nanowires 13.3.1 Symmetric InSb Nanowire Photodetectors 13.3.2 Asymmetric InSb Nanowire Photodetectors 13.4 Summary ReferencesChapter 14 Carbon Nanotube-Based Infrared Camera Using Compressive Sensing 14.1 Introduction 14.2 Theoretical Foundation of Compressive Sensing 14.2.1 General Idea 14.2.2 Sparsity 14.2.3 Restricted Isometry Property 14.2.4 Random Matrix 14.2.5 Compressive Sensing Applications 14.3 Compressive Sensing for Single-Pixel Photodetectors 14.3.1 System Architecture 14.3.2 Measurement Matrix 14.3.3 Data Sampling and Image Reconstruction Algorithm 14.4 Experimental Setup and Results 14.4.1 Static Measurement 14.4.2 Dynamic Observation 14.4.3 Performance Analysis 14.5 Summary and Perspectives ReferencesIndex