This book provides readers with an introduction to the materials and devices necessary for flexible sensors and electronics, followed by common techniques for fabrication of such devices and system-level integration. Key insights into fabrication and processing will guide readers through the tradeoff choices in designing such platforms. A comprehensive review of two specific, flexible bioelectronic platforms, related to smart bandages for wound monitoring and thread-based diagnostics for wearable health, will demonstrate practical application at the system level. The book also provides a unique electrical engineering perspective by reviewing circuit architectures for low noise signal conditioning of weak signals from sensors,, and for low power analog to digital converters for signal acquisition. To achieve energy autonomy, authors provide several example of CMOS energy harvesting front end circuits and voltage boosters. Beyond circuit architectures, the book also provides a review of the modern theory of sampling and recovery of sparse signals, also known as compressed sensing. They then highlight how these principles can be leveraged for design and implementation of efficient signal acquisition hardware and reliable processing of acquired data for flexible electronic platforms.
Author(s): Sameer Sonkusale, Maryam Shojaei Baghini, Shuchin Aeron
Publisher: Springer
Year: 2022
Language: English
Pages: 186
City: Cham
Preface: Introduction and Overview of Flexible Bioelectronics
Motivation for This Book
Flexible Bioelectronics
Contents of This Book
Acknowledgments
Contents
1 Materials and Processing for Flexible Bioelectronics
1.1 Materials for Flexible Bioelectronics
1.2 Substrates
1.2.1 Polymeric Materials
1.2.2 Fibers/Textiles
1.3 Electronic Functional Materials
1.3.1 Organic Materials
1.3.2 Metal Oxides
1.3.3 Carbon-based Materials
1.3.4 Conductive Inks and Liquid Metals
1.4 Materials Processing
1.4.1 Printing from Inks
1.4.2 Transfer Printing
1.5 Flexible Devices and Components
1.5.1 Electrodes
1.5.2 Transistors
1.6 Packaging and Integration
1.6.1 Methods
1.6.2 Package Assembly on Silicon CMOS Platform
References
2 Sensors and Platforms for Flexible Bioelectronics
2.1 Introduction
2.1.1 Flexible Biosensors
2.1.2 Sensing Principle
2.1.2.1 Optical Transducer
2.1.2.2 Electrochemical Transducer
2.1.3 Thermal Transducer
2.1.4 Piezoelectric Transducer
2.2 Characteristics of Biosensors
2.2.1 Physical Sensors
2.2.1.1 Physical Sensors for Monitoring Vital Signs
2.2.1.2 Physical Sensors for Measuring Biopotential Signals
2.2.1.3 Pressure and Strain Sensor
2.2.2 Chemical/Biological Sensors
2.2.3 Fully Integrated Wearable Bioflexible Platform
2.2.4 Smart Bandages for Wound Healing and Therapy
2.2.4.1 Smart Bandage for Wound Monitoring
2.2.4.2 Smart Bandage for On-demand Drug Delivery
2.2.4.3 Temperature-Triggered Drug Delivery
2.2.4.4 pH-Triggered Drug Delivery
2.3 Thread-Based Wearable/Implantable Diagnostics
2.3.1 Thread-Based Microfluidics
2.3.2 Thread-Based Sensors
2.3.3 Thread-Based Drug Delivery
2.3.4 Thread-Based Transistor and Circuits
2.4 Challenges and Outlook
References
3 Low-Noise CMOS Signal Conditioning Circuits
3.1 Introduction to Discrete and CMOS Signal Conditioning
3.1.1 Sensors
3.1.1.1 Capacitive Sensors
3.1.1.2 Resistive Sensors
3.1.1.3 Electrical Equivalent of R-C Sensors
3.2 Signal Conditioning Circuits
3.3 Mismatch and Device Noise Reduction Techniques
3.3.1 Auto-Zeroing
3.3.1.1 Open-Loop Offset Cancellation
3.3.1.2 Closed-Loop Offset Cancellation
3.3.1.3 Closed-Loop Offset Cancellation Using an Auxiliary Amplifier
3.3.2 Chopping
3.3.2.1 Voltage-Mode and Current-Mode Chopper Modulation Technique
3.3.2.2 Dual Chopping Technique
3.4 Interface Circuit Examples
3.4.1 Interfacing for Resistive Sensors
3.4.1.1 Auto-Nulling Technique for Resistive Sensors
3.4.1.2 Relaxation Oscillator Based Circuit for Resistive Sensors
3.4.2 Interfacing for Capacitive Sensors
3.4.2.1 Phase-Sensitive-Detection Technique for Capacitance Measurement
3.4.2.2 Auto-Nulling Based Signal Conditioning Circuit for Capacitive Sensors
3.4.3 Interfacing for Impedance R-C Sensors
3.4.3.1 Phase-Sensitive-Detection Based Impedance-to-Voltage Converter
3.4.4 Interface Excitation Techniques
3.5 Low-Power Low-Noise CMOS Instrumentation Amplifiers
3.5.1 3-Op-Amp Based Instrumentation Amplifier
3.5.2 Capacitive Feedback Instrumentation Amplifier
3.5.3 Current-Mode Instrumentation Amplifiers
References
4 Data Converters for Wearable Sensor Applications
4.1 Introduction
4.2 Low-Voltage ADC for Bio-Potential Acquisition
4.2.1 Delta-Sigma Modulators
4.2.2 Figure-of-Merits for Modulators
4.2.3 DSMs with Inverter Based Active Integrators
4.2.4 Passive Integrator Based DSMs
4.2.5 VCO Based DSMs
4.2.6 Performance Comparison of the DSMs
4.3 Direct-Digital Converters for Wearable Sensor Applications
4.3.1 Resistance-to-Digital Converter
4.3.1.1 Microcontroller Based Direct-Digital Converter for Resistive Sensor
4.3.1.2 Incremental Delta-Sigma Based Direct-Digital Converter for Resistive Sensors
4.3.2 Capacitance-to-Digital Converters
4.3.2.1 Dual-Slope Based Capacitance-to-Digital Converter
4.3.2.2 Direct-Digital Converter for Leaky Capacitive Sensors
4.3.3 Impedance-to-Digital Converters
4.3.3.1 Dual-Slope Based Impedance-to-Digital Converter
4.3.3.2 Delta-Sigma Based Bioimpedance-to-Digital Converter
References
5 Power Management Circuits for Energy Harvesting
5.1 Introduction to Energy Harvesting from Ambient
5.1.1 Maximum Power Point Tracking Circuits
5.1.1.1 Customized Boost and Buck Converters
5.1.1.2 Start-Up Circuit and Voltage Monitoring
5.1.2 Optimal Design of Boost Converter for MPPT
5.1.2.1 Ohmic Loss Calculation
5.1.2.2 Switching Loss
5.1.2.3 Minimizing Total Loss
5.1.2.4 MPPT Implementation by the Boost Converter
5.1.2.5 Sensing Vref Voltage
5.1.2.6 (D1T)opt Implementation
5.1.2.7 Synchronous Rectification Control for the Boost Converter
5.1.2.8 Calculation of the First Storage Capacitor, Cpool
5.1.3 Design of the Buck Converter as Output Voltage Regulator
5.1.4 Inductor Sharing Between MPPT and Voltage Regulator
References
6 Sampling and Recovery of Signals with Spectral Sparsity
6.1 Introduction
6.2 Is Sampling at Nyquist Rate Necessary?
6.3 Sampling Signals with Finite Rate of Innovation (FRI)
6.3.1 Extensions and Operational Considerations
6.4 Applications to Biomedical Signal Sampling and Recovery
References
7 Compressed Sensing
7.1 Introduction
7.2 What Is Compressed Sensing?
7.3 1 -0 Equivalence
7.4 Compressed Sensing with Analysis Sparsity
7.5 Algorithms for Solving the 1 Minimization Problem
7.6 Analog-to-Information Converters Based on CS
7.7 Application to Wearable and Flexible Biomedical Circuits and Systems
7.8 Application to Line Spectral Estimation
7.8.1 CS Based High Resolution Systems for Gravimetric MEMS Applications
7.9 Latest Trends and Looking Ahead
References
Index
