CMOS Integrated Lab-on-a-chip System for Personalized Biomedical Diagnosis

Inbunden, Engelska, 2018

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Beskrivning

A thorough examination of lab-on-a-chip circuit-level operations to improve system performanceA rapidly aging population demands rapid, cost-effective, flexible, personalized diagnostics. Existing systems tend to fall short in one or more capacities, making the development of alternatives a priority. CMOS Integrated Lab-on-a-Chip System for Personalized Biomedical Diagnosis provides insight toward the solution, with a comprehensive, multidisciplinary reference to the next wave of personalized medicine technology.A standard complementary metal oxide semiconductor (CMOS) fabrication technology allows mass-production of large-array, miniaturized CMOS-integrated sensors from multi-modal domains with smart on-chip processing capability. This book provides an in-depth examination of the design and mechanics considerations that make this technology a promising platform for microfluidics, micro-electro-mechanical systems, electronics, and electromagnetics.From CMOS fundamentals to end-user applications, all aspects of CMOS sensors are covered, with frequent diagrams and illustrations that clarify complex structures and processes. Detailed yet concise, and designed to help students and engineers develop smaller, cheaper, smarter lab-on-a-chip systems, this invaluable reference: Provides clarity and insight on the design of lab-on-a-chip personalized biomedical sensors and systemsFeatures concise analyses of the integration of microfluidics and micro-electro-mechanical systemsHighlights the use of compressive sensing, super-resolution, and machine learning through the use of smart SoC processingDiscusses recent advances in complementary metal oxide semiconductor-integrated lab-on-a-chip systemsIncludes guidance on DNA sequencing and cell counting applications using dual-mode chemical/optical and energy harvesting sensorsThe conventional reliance on the microscope, flow cytometry, and DNA sequencing leaves diagnosticians tied to bulky, expensive equipment with a central problem of scale. Lab-on-a-chip technology eliminates these constraints while improving accuracy and flexibility, ushering in a new era of medicine. This book is an essential reference for students, researchers, and engineers working in diagnostic circuitry and microsystems.

Produktinformation

Utgivningsdatum:2018-07-10
Mått:150 x 229 x 13 mm
Vikt:590 g
Format:Inbunden
Språk:Engelska
Serie:IEEE Press
Antal sidor:304
Förlag:John Wiley & Sons Inc
ISBN:9781119218326

Utforska kategorier

Primärvård inom Medicin

Mer om författaren

Hao Yu, Southern University of Science and Technology, China, is an assistant professor and area director of the VIRTUS/VALENS Centre of Excellence. Mei Yan, Consultant, developed lab-on-a-chip biomedical sensor circuits and systems for personalized biomedical diagnosis as a Research Fellow at Nanyang Technological University in Singapore. Xiwei Huang, Hangzhou Dianzi University, China, is an assistant professor at the School of Electronics and Information.

Innehållsförteckning

Preface x1 Introduction 11.1 Personalized Biomedical Diagnosis 11.1.1 Personalized Diagnosis 11.1.2 Conventional Biomedical Diagnostic Instruments 31.1.2.1 Optical Microscope 31.1.2.2 Flow Cytometer 41.1.2.3 DNA Sequencer 51.2 CMOS Sensor‐based Lab‐on‐a‐Chip for System Miniaturization 71.2.1 CMOS Sensor‐based Lab‐on‐a‐Chip 71.2.2 CMOS Sensor 81.2.2.1 CMOS Process Fundamentals 81.2.2.2 CMOS Sensor Technology 101.2.2.3 Multimodal CMOS Sensor 131.2.3 Microfluidics 141.2.3.1 Microfluidic Fundamentals 141.2.3.2 Microfluidics Fabrication 161.3 Objectives and Organization of this Book 201.3.1 Objectives 201.3.2 Organization 20References 212 CMOS Sensor Design 252.1 Top Architecture 252.2 Noise Overview 252.2.1 Thermal Noise 262.2.2 Flicker Noise 272.2.3 Shot Noise 282.2.4 MOSFET Noise Model 292.3 Pixel Readout Circuit 292.3.1 Source Follower 302.3.2 Sub‐threshold Gm Integrator 332.3.3 Ctia 352.4 Column Amplifier 382.5 Column ADC 392.5.1 Single‐Slope ADC 392.5.2 Sigma‐Delta ADC 432.6 Correlated Sampling 492.6.1 Correlated Double Sampling 492.6.2 Correlated Multiple Sampling 512.7 Timing Control 522.7.1 Row Timing Control 522.7.2 Column Timing Control 552.8 LVDS Interface 57References 593 CMOS Impedance Sensor 603.1 Introduction 603.2 CMOS Impedance Pixel 613.3 Readout Circuit 633.4 A 96 × 96 Electronic Impedance Sensing System 653.4.1 Top Architecture 653.4.2 System Implementation 673.4.2.1 System Setup 673.4.2.2 Sample Preparation 683.4.3 Results 683.4.3.1 Data Fitting for Single Cell Impedance Measurement 693.4.3.2 Cell and Electrode Impedance Analysis 713.4.3.3 EIS for Single‐Cell Impedance Enumeration 71References 744 CMOS Terahertz Sensor 764.1 Introduction 764.2 CMOS THz Pixel 764.2.1 Differential TL‐SRR Resonator Design 764.2.1.1 Stacked SRR Layout 764.2.1.2 Comparison with Single‐ended TL‐SRR Resonator 804.2.1.3 Comparison with Standing‐Wave Resonator 824.2.2 Differential TL‐CSRR Resonator Design 834.3 Readout Circuit 844.3.1 Super‐regenerative Amplification 844.3.1.1 Equivalent Circuit of SRA 844.3.1.2 Frequency Response of SRA 864.3.1.3 Sensitivity of SRA 864.3.2 Super‐regenerative Receivers 874.3.2.1 Quench‐controlled Oscillation 874.3.2.2 SRX Design by TL‐CSRR 894.3.2.3 SRX Design by TL‐SRR 914.4 A 135 GHz Imager 944.4.1 135 GHz DTL‐SRR‐based Receiver 944.4.2 System Implementation 954.4.3 Results 954.5 Plasmonic Sensor for Circulating Tumor Cell Detection 984.5.1 Introduction of CTC Detection 984.5.2 SRR‐based Oscillator for CTC Detection 994.5.3 Sensitivity of SRR‐based Oscillator 101References 1035 CMOS Ultrasound Sensor 1065.1 Introduction 1065.2 CMUT Pixel 1075.3 Readout Circuit 1095.4 A 320 × 320 CMUT‐based Ultrasound Imaging System 1105.4.1 Top Architecture 1105.4.2 System Implementation 1115.4.2.1 Process Selection 1115.4.2.2 High Voltage Pulser 1125.4.2.3 Low‐Noise Preamplifier and High Voltage Switch 1155.4.3 Results 1165.4.3.1 Simulation Results 1165.4.3.2 Two‐channel AFE IC Measurement Results 1175.4.3.3 Acoustic Transmission Testing with AFE IC and CMUT 1215.4.3.4 Acoustic Pulse‐echo Testing with AFE IC and CMUT 122References 1246 CMOS 3‐D‐Integrated MEMS Sensor 1266.1 Introduction 1266.2 MEMS Sensor 1276.3 Readout Circuit 1276.4 A 3‐D TSV‐less Accelerometer 1296.4.1 CMOS‐on‐MEMS Stacking 1296.4.2 Bonding Reliability 1326.4.2.1 Al–Au Thermo‐compression Shear Strength 1326.4.2.2 Al–Au Thermo‐compression Hermeticity 1346.4.3 Results 1356.4.3.1 Standalone Validation of the Readout Circuit 1356.4.3.2 Functionality Testing of CMOS‐on‐MEMS Chip 1366.4.3.3 Reliability Testing of CMOS‐on‐MEMS Chip 138References 1417 CMOS Image Sensor 1427.1 Introduction 1427.2 CMOS Image Pixel 1457.2.1 Structure 1457.2.1.1 FSI 4 T Pixel 1457.2.1.2 Back Side Illumination Pixel 1477.2.1.3 Stack Pixel 1487.2.2 Noise and Model 1507.2.2.1 Photon Shot Noise 1517.2.2.2 Reset Noise 1527.2.2.3 Thermal Noise 1527.2.2.4 Flicker Noise 1547.2.2.5 Fixed Pattern Noise 1547.3 Readout Circuit 1557.3.1 Global Serial Readout 1567.3.2 Correlated Double Sampling 1567.4 A 3.2 Mega CMOS Image Sensor 1587.4.1 4‐way Shared Pixel Unit 1587.4.2 Top Architecture 1597.4.3 System Implementation 1627.4.4 Results 1647.4.4.1 System Characterization 1647.4.4.2 Digital CDS for FPN Reduction 1647.4.4.3 Blood Cell Imaging Experiments 165References 1678 CMOS Dual‐mode pH‐Image Sensor 1698.1 Introduction 1698.2 CMOS Dual‐mode pH‐Image Pixel 1708.3 Readout Circuit 1728.3.1 CDS for Optical Sensing 1748.3.2 CDS for Chemical Sensing 1748.4 A 64 × 64 Dual‐mode pH‐Image Sensor 1758.4.1 Top Architecture 1758.4.2 System Implementation 1778.4.3 Results 177References 1849 CMOS Dual‐mode Energy‐harvesting‐image Sensor 1869.1 Introduction 1869.2 CMOS EHI Pixel 1879.3 Readout Circuit 1919.4 A 96 × 96 EHI Sensing System 1959.4.1 Top Architecture 1959.4.2 System Implementation 1979.4.3 Results 203References 21110 DNA Sequencing 21310.1 Introduction 21310.2 CMOS ISFET‐based Sequencing 21310.2.1 Overview 21310.2.2 ISFET‐based Sequencing Procedure 21510.3 CMOS THz‐based Genotyping 22010.3.1 Overview 22010.3.2 THz‐based Genotyping Procedure 22010.4 Beyond CMOS Nanopore Sequencing 22110.4.1 Overview 22110.4.2 Nanopore‐based Sequencing Procedure 22310.5 Summary 227References 23011 Cell Counting 23111.1 Introduction 23111.2 Optofluidic Imaging System 23111.2.1 Contact Imaging 23111.2.2 Optofluidic Imaging System Model 23211.2.2.1 Resolution Model 23211.2.2.2 Dynamic Range Model 23311.2.2.3 Implication to SR Processing 23411.3 Super‐resolution Image Processing 23411.3.1 Multi‐frame SR Processing 23511.3.2 Single‐frame SR Processing 23611.4 Machine‐learning‐based Single‐frame Super‐resolution 23711.4.1 Elmsr 23811.4.2 Cnnsr 24211.5 Microfluidic Cytometer for Cell Counting 24511.5.1 Microfluidic Cytometer System 24511.5.1.1 System Overview 24511.5.1.2 Microfluidic Channel Fabrication 24611.5.1.3 Microbead and Cell Sample Preparation 24611.5.1.4 Microfluidic Cytometer Design 24711.5.1.5 Cell Detection 24811.5.1.6 Cell Recognition 24911.5.1.7 Cell Counting 25011.5.2 Results 25011.5.2.1 Counting Performance Characterization 25011.5.2.2 Off-Line SR Training 25111.5.2.3 On‐line SR Testing 25311.5.2.4 On‐line Cell Recognition and Counting 254References 25512 Conclusion 25812.1 Summaries 25812.2 Future Works 260Index 262