Smart Sensor Systems

Professor Gerard C. M. Meijer, Electronic Instrumentation Laboratory, Delft University of Technology, the NetherlandsProfessor Meijer is currently a full professor of the Laboratory of Electronic Instrumentation at Delft University of Technology and since 1972 he has been a member of the Research and Teaching staff of the Faculty of Electrical Engineering. His main areas of research concern smart sensor systems and analog interface electronics. He has performed application-oriented research on sensor-interface circuits and fundamental research on the accuracy of voltage references, integrated temperature sensors, effects of mechanical stress in integrated circuits and the effects at high temperatures in integrated circuits.Professor Meijer chairs the national organization 'Sensorplatform' of the Dutch Technology Foundation STW, and the program 'Autonomous Sensor Systems' a national research program. His work has been published in over 280 papers and he has won numerous awards including 'Simon-Stevin Meester' honouree degree in 1999, and the 'Anthony van Leeuwenhoek' chair at TUdelft in 2001.Contributors:Bernhard Boser, University of California, BerkeleyJan Bosiers, Dalsa, the NetherlandsTim Denison, Medtronic, USAJohan Huijsing, TUDelft, the NetherlandsKofi Makinwa, TUDelft, the NetherlandsMichiel Pertijs, Holst Centre, the NetherlandsRoland Thewes, Infineon, GermanyTim Tiek, Sensata, the NetherlandsAlbert Theuwissen, TUDelft, the Netherlands

• About the Editors xiList of Contributors xiiiPreface xv1 Smart Sensor Design 1Kofi Makinwa1.1 Introduction 11.2 Smart Sensors 21.2.1 Interface Electronics 31.2.2 Calibration and Trimming 51.3 A Smart Temperature Sensor 51.3.1 Operating Principle 61.3.2 Interface Electronics 61.3.3 Recent Work 81.4 A Smart Wind Sensor 81.4.1 Operating Principle 81.4.2 Interface Electronics 101.4.3 Recent Work 111.5 A Smart Hall Sensor 111.5.1 Operating Principle 111.5.2 Interface Electronics 121.5.3 Recent Work 131.6 Conclusions 14References 142 Calibration and Self-Calibration of Smart Sensors 17Michiel Pertijs2.1 Introduction 172.2 Calibration of Smart Sensors 182.2.1 Calibration Terminology 182.2.2 Limited Validity of a Calibration 192.2.3 Specifics of Smart Sensor Calibration 202.2.4 Storing Calibration Data in the Sensor 202.2.5 Calibration in the Production Process 232.2.6 Opportunities for Smart Sensor Calibration 242.2.7 Case Study: A Smart Temperature Sensor 242.3 Self-Calibration 272.3.1 Limitations of Self-Calibration 272.3.2 Self-Calibration by Combining Multiple Sensors 272.3.3 Self-Calibrating Sensactors 302.3.4 Case Study: A Smart Magnetic Field Sensor 312.3.5 Null-Balancing Sensactors 332.3.6 Case Study: A Smart Wind Sensor 352.3.7 Other Self-Calibration Approaches 362.4 Summary and Future Trends 382.4.1 Summary 382.4.2 Future Trends 39References 403 Precision Instrumentation Amplifiers 42Johan Huijsing3.1 Introduction 423.2 Applications of Instrumentation Amplifiers 433.3 Three-OpAmp Instrumentation Amplifiers 443.4 Current-Feedback Instrumentation Amplifiers 463.5 Auto-Zero OpAmps and InstAmps 483.6 Chopper OpAmps and InstAmps 523.7 Chopper-Stabilized OpAmps and InstAmps 563.8 Chopper-Stabilized and AZ Chopper OpAmps and InstAmps 623.9 Summary and Future Directions 65References 664 Dedicated Impedance-Sensor Systems 68Gerard Meijer, Xiujun Li, Blagoy Iliev, Gheorghe Pop, Zu-Yao Chang, Stoyan Nihtianov, Zhichao Tan, Ali Heidari and Michiel Pertijs4.1 Introduction 684.2 Capacitive-Sensor Interfaces Employing Square-Wave Excitation Signals 714.2.1 Measurement of Single Elements 714.2.2 Energy-Efficient Interfaces Based on Period Modulation 714.2.3 Measurement of Capacitive Sensors with High Speed and High Resolution 744.2.4 Measurement of Grounded Capacitors: Feed-Forward Active Guarding 764.3 Dedicated Measurement Systems: Detection of Micro-Organisms 784.3.1 Characterization of Conductance Changes Due to Metabolism 784.3.2 Impedance Measurements with a Relaxation Oscillator 814.4 Dedicated Measurement Systems: Water-Content Measurements 834.4.1 Background 834.4.2 Capacitance Versus Water Content 834.4.3 Skin and Proximity Effects 854.4.4 Dedicated Interface System for Water-Content Measurements 874.5 Dedicated Measurement Systems: A Characterization System for Blood Impedance 894.5.1 Characteristics of Blood and Electrical Models 894.5.2 In-vivo Blood Analysis System 924.5.3 Experimental Results 954.6 Conclusions 97References 985 Low-Power Vibratory Gyroscope Readout 101Chinwuba Ezekwe and Bernhard Boser5.1 Introduction 1015.2 Power-Efficient Coriolis Sensing 1015.2.1 Review of Vibratory Gyroscopes 1025.2.2 Electronic Interface 1025.2.3 Readout Interface 1035.2.4 Improving Readout Interface Power Efficiency 1055.2.5 Exploiting the Sense Resonance 1065.3 Mode Matching 1085.3.1 Estimating the Mismatch 1095.3.2 Tuning Out the Mismatch 1125.3.3 Closing the Tuning Loop 1145.3.4 Practical Considerations 1165.4 Force Feedback 1195.4.1 Mode-Matching Consideration 1195.4.2 Preliminary System Architecture and Model for Stability Analysis 1205.4.3 Accommodating Parasitic Resonances 1215.4.4 Positive Feedback Architecture 1265.5 Experimental Prototype 1335.5.1 Implementation 1335.5.2 Experimental Results 1385.6 Summary 142References 1436 Introduction to CMOS-Based DNA Microarrays 145Roland Thewes6.1 Introduction 1456.2 Basic Operation Principle and Application of DNA Microarrays 1466.3 Functionalization 1496.4 CMOS Integration 1506.5 Electrochemical Readout Techniques 1536.5.1 Detection Principles 1536.5.2 Potentiometric Setup 1606.5.3 Readout Circuitry 1626.6 Further Readout Techniques 1656.6.1 Labeling-Based Approaches 1656.6.2 Label-Free Approaches 1666.7 Remarks on Packaging and Assembly 1696.8 Concluding Remarks and Outlook 169References 1707 CMOS Image Sensors 173Albert Theuwissen7.1 Impact of CMOS Scaling on Image Sensors 1737.2 CMOS Pixel Architectures 1757.3 Photon Shot Noise 1807.4 Analog-to-Digital Converters for CMOS Image Sensors 1817.5 Light Sensitivity 1847.6 Dynamic Range 1867.7 Global Shutter 1877.8 Conclusion 188Acknowledgment 188References 1888 Exploring Smart Sensors for Neural Interfacing 190Tim Denison, Peng Cong and Pedram Afshar8.1 Introduction 1908.2 Technical Considerations for Designing a Dynamic Neural Control System 1928.3 Predicate Therapy Devices Using Smart-Sensors in a Dynamic Control Framework: Lessons Derived from Closed-Loop Cardiac Pacemakers 1958.4 The Application of “Indirect” Smart Sensing Methods: A Case Study of Posture Responsive Spinal Cord Stimulation for Chronic Pain 1988.4.1 Overview of the Posture Responsive Control System 1988.4.2 The Design Challenge: Defining the Desired Patient State 1988.4.3 The Physical Sensor: Three Axis Accelerometer 2008.4.4 Design Details of the Three-Axis Accelerometer 2028.4.5 Making the Sensor “Smart” with State Estimation: The Position Detection Algorithm and Titration Algorithm 2058.4.6 “Closing the Loop”: Mapping Inertial-Information to Stimulation Parameters for Posture-Based Adaptive Therapy 2068.5 Direct Sensing of Neural States: A Case Study in Smart Sensors for Measurement of Neural States and Enablement of Closed-Loop Neural Systems 2078.5.1 Implantable Bidirectional Brain-Machine-Interface System Design 2098.5.2 Design Overview of a Chopper Stabilized EEG Instrumentation Amplifier 2108.5.3 Exploration of Neural Smart Sensing in the Brain: Prototype Testing in an Animal Models 2198.5.4 Demonstrating the Concepts of Smart Sensing in the Brain: Real-Time Brain-State Estimation and Stimulation Titration 2248.6 Future Trends and Opportunities for Smart Sensing in the Nervous System 231Disclosure 233References 2339 Micropower Generation: Principles and Applications 237Ruud Vullers, Ziyang Wang, Michael Renaud, Hubregt Visser, Jos Oudenhoven and Valer Pop9.1 Introduction 2379.2 Energy Storage Systems 2409.2.1 Introduction 2409.2.2 Supercapacitors 2419.2.3 Lithium-Ion Batteries 2429.2.4 Thin-Film Lithium-Ion Batteries 2449.2.5 Energy Storage Applications 2459.3 Thermoelectric Energy Harvesting 2469.3.1 Introduction 2469.3.2 State-of-the-Art 2479.3.3 Conversion Efficiency 2529.3.4 Power Management 2529.3.5 Conclusion 2539.4 Vibration and Motion Energy Harvesting 2539.4.1 Introduction 2539.4.2 Machine Environment: Resonant Systems 2549.4.3 Human Environment: Non-Resonant Systems 2599.4.4 Power Management 2619.4.5 Summary 2619.5 Far-Field RF Energy Harvesting 2629.5.1 Introduction 2629.5.2 General principle 2629.5.3 Analysis and Design 2659.5.4 Application 2669.6 Photovoltaic 2689.7 Summary and Future Trends 2689.7.1 Summary 2689.7.2 Future Trends 270References 270Index 275