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Smart Chips for Smart Devices

VLSI Design for Next-Generation IoT Solutions
Edited by Abhishek Kumar and Suman Lata Tripathi
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394335800 | Hardcover |
366 pages
Price: $225 USD

One Line Description
Master the future of semiconductor technology with this essential guide, which provides the advanced circuit design strategies necessary to develop the high-performance, low-power VLSI chips that drive the modern IoT revolution.

Audience
Academics, computer science engineers, IoT device manufacturers, and telecom and other industry professionals working in electronics and communication engineering.

Description
The IoT revolution has created a demand for ever-more-sophisticated and compact devices, making VLSI design a significant concern. The demand for low-power, efficient, portable, and reliable semiconductor chips forces research into developing new designs and techniques to cater to these advanced applications. This book explores the state-of-the-art field of Very-Large-Scale Integration design, emphasizing how it is used in the Internet of Things. It demonstrates strategies that combine VLSI in IoT applications, covering a wide range of subjects that are crucial for creating integrated circuits that are dependable, powerful, and high-performing. The book explores advanced circuit design, system-on-chip (SoC) architectures, and basic VLSI design ideas. Additionally, it addresses important aspects of IoT applications, like sensor integration, low power consumption, and secure communication protocols through case studies and practical applications that show how theoretical ideas are put to use in real-world settings to address the unique requirements of IoT systems. Designed for engineers, designers, and researchers, this book is an essential guide for using VLSI principles to make more creative, scalable, and effective Internet of Things solutions.

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Author / Editor Details
Abhishek Kumar, PhD is an Associate Professor in the School of Electronics and Electrical Engineering at Lovely Professional University with more than 11 years of experience. He has published more than 55 research papers in respected journals, five books, and 20 patents, contributed to numerous conference proceedings and e-books, and registered five copyrights. His research interests include hardware security, cryptanalysis, and machine learning.

Suman Lata Tripathi, PhD is a Professor at Lovely Professional University with more than 22 years of experience in academics and research. She has published more than 125 research papers in refereed journals, conference proceedings, and e-books, 27 books, 14 Indian patents, and four copyrights. Her area of expertise includes microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design testing, and advanced FET design for IoT.

Table of Contents
Preface
1. Integration of Nanotechnology and Machine Learning in Healthcare: Fundamentals, Applications, and Future Scope
Bindu S., Prashanth M. K. and Mohan Kishore D.
1.1 Introduction
1.1.1 Overview of Machine Learning (ML) and Its Impact on Modern Technology
1.2 Nanotechnology and Its Impact on Healthcare
1.3 The Role of Machine Learning in Advancing Nanotechnology
1.3.1 Objectives of Integrating ML in Healthcare through Nanotechnology
1.4 Fundamentals of Nanotechnology in Healthcare
1.4.1 Key Nanomaterials in Healthcare
1.4.1.1 Nanoparticles
1.4.1.2 Nanotubes
1.4.1.3 Nanostructures
1.4.1.4 Quantum Dots
1.4.1.5 Dendrimers
1.4.2 Machine Learning: Overview and Key Concepts
1.5 Intersection of Machine Learning and Nanotechnology
1.5.1 ML Techniques in Design of Nanotechnology Complementing Each Other
1.5.2 Predictive Modeling for Nanomaterial Behavior
1.5.2.1 Predictive Modeling
1.5.2.2 Nanoparticle-Cell Interactions
1.5.2.3 Nanotoxicology
1.5.2.4 Drug Release Dynamics
1.6 Generative Modeling for Nanomaterial Behavior
1.7 Applications in Healthcare
1.8 Application of AI with Aid of Nanotechnology
1.9 Dental Cleansing Robots
1.10 Challenges in Nanotechnology for Healthcare
1.11 Case Studies
1.11.1 Smart Nanocarriers for Cancer Therapy
1.11.2 Early Diagnosis of Neurodegenerative Diseases
1.11.3 COVID-19 Vaccine Delivery Optimization
1.11.4 Nanobiosensors for Diabetes Management
1.12 Advantages and Challenges
1.12.1 Advantages
1.12.2 Challenges
1.13 Future Trends
1.14 Conclusion
References
2. FPGA-Based Hardware Accelerators for ANN and DNN
Abhishek Kumar
2.1 Introduction
2.2 Hardware Accelerator for FPGA
2.3 Neural Network Accelerator
2.4 Artificial Neural Networks (ANN) Accelerator
2.5 Deep Neural Network (DNN) Accelerator
2.6 Discussion
2.7 Conclusion
References
3. Analysis of Double-Gate Tunnel FET Using AI and ML
Siga Karthik and Sanjeet Kumar Sinha
3.1 Introduction
3.2 Double-Gate Tunnel FET (DG-TFET)
3.2.1 Basic Structure and Operation
3.2.2 Advantages and Challenges
3.3 Role of AI and ML in DG-TFET Analysis
3.3.1 High-Dimensional Data Handling
3.3.2 Better Predictions
3.3.3 Reduced Simulation Time
3.4 Applications of AI and ML in DG-TFETs
3.4.1 Current-Voltage Predictions
3.4.2 Optimization of Device Parameters
3.4.3 Accelerating Simulations
3.5 Machine Learning Models for DG-TFET Analysis
3.6 Unsupervised Learning for Pattern Recognition
3.7 Reinforcement Learning for Device Optimization
3.8 Case Study: AI-Driven DG-TFET Optimization
3.8.1 Dataset Collection
3.8.2 Model Training and Validation
3.9 Results and Insights
3.10 Conclusion
References
4. NC-TFET Device and Its Applications in Future VLSI
Katuri Yeshwant and Sanjeet Kumar Sinha
4.1 Introduction
4.2 Basics of Tunnel Field Effect Transistor (TFET)
4.2.1 Working Principle of TFET
4.2.2 Band-to-Band Tunneling (BTBT)
4.2.3 TFET Advantages
4.2.4 TFET Challenges
4.3 Negative Capacitance Effect
4.3.1 NC-TFET Device Architecture
4.3.2 Key Components of NC-TFET
4.3.3 Materials for Negative Capacitance
4.4 Theoretical Benefits of NC-TFET
4.4.1 Reduced Subthreshold Swing (SS)
4.4.2 Lower Power Consumption
4.4.3 Enhanced Scalability
4.5 NC-TFET Fabrication and Process Integration
4.5.1 Challenges in Fabrication
4.5.2 Integration with CMOS Processes
4.6 Applications of NC-TFET
4.7 Future Prospects and Research Directions
4.8 Conclusion
References
5. Low-Power VLSI Design Techniques for High-Performance Applications
Ashutosh Kumar Yadav, Sweta Chander, Suman Lata Tripathi and Sanjeet Kumar Sinha
5.1 Introduction
5.1.1 The Imperative for Low-Power Design
5.1.1.1 Portable Devices
5.1.1.2 Data Centers
5.1.1.3 IoT Devices
5.1.1.4 High-Performance Computing (HPC)
5.2 Applications of Low-Power VLSI Design in High-Performance Systems
5.2.1 Mobile Computing
5.2.2 AI and Machine Learning Accelerators
5.2.3 Data Centers and Cloud Computing
5.2.4 Internet of Things (IoT)
5.3 Importance of Low-Power VLSI Design
5.4 Scope of the Chapter
5.5 Fundamentals of Power Consumption in VLSI Circuits
5.5.1 Dynamic Power Consumption
5.5.2 Static Power Consumption
5.5.3 Short-Circuit Power Consumption
5.6 Low-Power Design Techniques
5.6.1 Technology-Level Low-Power Design Approaches
5.6.2 Multi-Threshold CMOS (MTCMOS)
5.6.3 Silicon-On-Insulator (SOI) Technology
5.6.4 FinFETs and Gate-All-Around (GAA) Transistors
5.7 Low Power Design Technique and Power Management
5.7.1 Clock Gating
5.7.2 Power Gating
5.7.3 Dynamic Voltage and Frequency Scaling (DVFS)
5.7.4 Adaptive Body Biasing (ABB)
5.8 Low-Power VLSI Design Methodologies
5.8.1 Power-Efficient VLSI through Technology-Level Design
5.8.2 Low-Power VLSI: Circuit-Level Power Optimization
5.8.3 Architecture-Level Power Reduction Strategies
5.8.4 Impact of Scaling on Leakage and Device Variability
5.8.5 Technology-Driven Low-Power Design Strategies
5.8.6 Scaling and Moore’s Law
5.8.7 Increased Leakage Power
5.8.8 Increased Variability
5.9 Pipelining and Parallelism for Energy-Efficient VLSI Design
5.9.1 Pipelining and Parallelism
5.9.2 Voltage Islands and Power Domains
5.9.3 Low-Power Memory Design
5.10 Efficient Processor Design
5.10.1 Out-of-Order Execution
5.10.2 Superscalar Pipelines
5.10.3 Specialized Cores
5.10.4 Architectures and Specialized Processing Cores
5.11 Energy-Efficient Algorithms and Approximate Computing
5.11.1 Energy-Efficient Algorithms
5.11.2 Approximate Computing for Low-Power VLSI Design
5.11.3 Applications
5.12 Advanced Techniques for Low-Power Design
5.12.1 Approximation-Aware Circuit Design for Power Optimization
5.12.2 Sub-Threshold Design
5.12.3 Machine Learning for Power Optimization
5.12.4 Hardware Acceleration for Low-Power Computing
5.12.5 Optimizing Memory Hierarchies for Power and Performance
5.12.6 Sources of Power Dissipation in Memory Systems
5.12.7 Dynamic Power
5.12.8 Static (Leakage) Power
5.12.9 Refresh Power
5.12.10 Data Movement Power
5.12.11 Techniques for Low-Power Memory Design
5.12.12 Memory Cell-Level Techniques
5.12.13 Low-Power SRAM Design
5.12.14 Reducing Leakage in SRAM
5.12.15 Low-Power DRAM Design
5.12.16 Reducing Refresh Rate
5.12.17 Sub-Array Activation
5.12.18 Low-Power Non-Volatile Memory (NVM) Design
5.12.19 PCM and MRAM
5.13 Conclusion
5.14 Future Directions
References
6. The Role of Artificial Intelligence and Machine Learning in Modern Healthcare Systems
Anudeep Goraya
6.1 Introduction
6.2 Overview of AI and ML Technologies in Healthcare
6.2.1 Supervised Learning
6.2.2 Unsupervised Learning
6.2.3 Reinforcement Learning
6.2.4 Deep Learning
6.3 AI in Diagnostic Systems
6.4 AI in Treatment and Precision Medicine
6.5 AI Patient Management and Support Systems
6.6 AI in Electronic Health Records (EHR) Management
6.7 AI for Virtual Health Assistants and Chatbots
6.8 Remote Monitoring and Telemedicine
6.9 Machine Learning in Healthcare Data Analytics
6.9.1 Importance of Data in AI and ML Models
6.9.2 Data Mining Techniques in Healthcare
6.9.3 Predicting Deterioration
6.10 Predictive Analytics and Big Data
6.11 Data Privacy, Security, and Ethical Considerations
6.12 Challenges and Limitations of AI and ML in Healthcare
6.13 AI Models’ Interpretability and Explainability
6.14 Conclusion
References
7. CMOS 90nm-Based Two-Stage Differential Amplifier
Lingampalli Abhijith, Hakke Mayuri Shahuraj, Sobhit Saxena and Suman Lata Tripathi
7.1 Introduction
7.2 Theory of Differential Amplifiers
7.2.1 Basic Principles of Operation
7.2.2 Differential Mode
7.2.3 Common Mode
7.2.4 Differential Gain and Common-Mode Rejection Ratio (CMRR)
7.3 Differential Amplifier
7.3.1 Single-Stage Differential Amplifier
7.3.2 Limitations of a Single-Stage Amplifier
7.3.3 Two-Stage Differential Amplifier
7.3.4 Advantages of Two-Stage Over Single-Stage Amplifiers
7.3.5 Common-Mode Topology in Two-Stage Differential Amplifier
7.3.6 The Differential-Mode Topology in Two-Stage Differential Amplifier
7.3.7 Importance of Matching and Symmetry in Differential-Mode Operation
7.3.8 Impact of the Second Stage on Differential Gain
7.4 Design Considerations
7.4.1 Biasing Strategies for Two-Stage Amplifiers
7.4.2 Applications of Two-Stage Differential Amplifiers
7.5 Conclusion
7.6 Future Trends and Considerations
References
8. Smart Self-Powered Wireless Sensor Network for Industrial Monitoring
G. Thirumalaiah and Mudassir Khan
8.1 Introduction
8.2 Related Works
8.2.1 Cloud Computation with ThingSpeak
8.3 Existing System
8.4 Proposed System
8.5 Hardware Components
8.5.1 12V Battery
8.5.2 Piezoelectric Sensor
8.5.3 Solar Panel
8.5.4 Power Supply
8.5.5 AC Power (Adapter)
8.5.6 Relay
8.5.7 Raspberry Pi
8.5.8 DHT11 (Temperature/Humidity) Sensor
8.5.9 Dallas Temperature Sensor
8.5.10 MQ135 Gas Sensor
8.5.11 CPU Fan
8.5.12 GSM Module
8.5.13 Motor Driver (L293D)
8.5.14 DC Motor
8.5.15 Push Buttons
8.5.16 LCD
8.5.17 LED
8.5.18 Buzzer
8.6 Software Components
8.6.1 Raspbian OS
8.6.2 Python
8.6.3 VNC Viewer
8.7 Step-by-Step Procedure
8.8 Experimental Setup and Results
8.9 Limitations
8.9.1 Cost Analysis and Feasibility
8.9.2 Graphical Representation and Comparative Analysis
8.9.3 Technical Limitations and Maintenance Challenges
8.10 Conclusion
References
9. Recent Advancements in High Electron Mobility Transistor-Based Monolithic Microwave Integrated Circuits: A Review
S.K. Hima Bindhu, Yogesh Kumar Verma and Kamal Bhatia
9.1 Introduction
9.2 Recent Advancements in HEMT-Based MMICs
9.3 Applications of Recent Advancements in HEMT MMIC
9.4 Conclusion
References
10. Ferroelectric Gated Field Effect Transistors
Ankush Mondal, Amandeep Singh, Sweta Chander and Sanjeet K. Sinha
10.1 Introduction
10.2 Ferroelectric Materials
10.2.1 Landau-Devonshire Theory of Phase Transition
10.2.2 Landau-Khalatnikov Equation and Dynamic Phase Transition
10.3 Ferroelectric Stack on Gate for Voltage Amplification
10.3.1 Common Short-Channel Effects at Lower Technology Nodes
10.3.1.1 Drain-Induced Barrier Lowering (DIBL)
10.3.1.2 Threshold Voltage Roll-Off
10.3.1.3 Velocity Saturation
10.3.1.4 Channel Length Modulation
10.3.1.5 Hot Carrier Effect (HCE)
10.3.1.6 Subthreshold Slope Degradation
10.3.1.7 Gate-Induced Drain Leakage (GIDL)
10.3.1.8 Punch Through
10.3.2 Ferroelectric Stacking
10.3.2.1 Negative Capacitance Effect
10.3.2.2 Charge Accumulation
10.4 Electrical Characterisation of MOSFET with Ferroelectric Stack Placement
10.4.1 Characteristics
10.4.2 Electric Field and Electric Potential Profiles
10.4.3 Current Density Profiles
10.4.4 Ferroelectric Polarization of the Fe-MOSFET
10.5 Further Considerations and Improvements
Acknowledgment
References
11. Design and Analysis of a Boolean Function Using CMOS-Based Logic Styles
Vanshika Nanda and Suman Lata Tripathi
11.1 Introduction
11.2 CMOS Logic Style
11.2.1 Pseudo Logic Style
11.2.2 Dynamic Logic Style
11.3 Performance Parameters
11.3.1 Leakage Current
11.3.2 On Current
11.3.3 Propagation Delay
11.3.4 Power Consumption
11.3.4.1 Static Power Consumption
11.3.4.2 Power Delay Product
11.4 Result Analysis
11.5 Advantages and Disadvantages
11.6 Conclusion
References
12. SAFETRACK: A Next-Generation Vehicle Tracking and Safety System Using Arduino and GPS
Manoj Kumar Yadav, Dinesh Singh and Jhulan Kumar
12.1 Introduction
12.1.1 Problem Statement
12.1.2 Proposed System
12.1.3 Existing System
12.2 System Architecture
12.2.1 Arduino
12.2.2 GSM Module
12.2.3 GPS Module
12.2.4 LM2596 Step Converter
12.2.5 ADXL-345 Sensor
12.2.6 Flow Diagram
12.3 Methodology
12.4 Results and Discussion
12.5 Conclusion
References
13. The Role of VLSI in Next-Generation IoT Systems
S. Deepa, S. Pavan Kumar, V. Arun and J. Lalidesh
13.1 Introduction
13.2 The Role of VLSI in IoT Data Processing
13.2.1 High-Speed Data Processing
13.2.1.1 Parallel Processing and Multicore Architectures
13.2.1.2 Specialized Computing Accelerators
13.2.2 Power Efficiency in IoT Devices
13.2.2.1 Low-Power Circuit Design Strategies
13.2.2.2 Energy-Efficient Transistors
13.2.3 Scalability and Integration
13.2.3.1 System-on-Chip (SoC) and System-in-Package (SiP) Designs
13.2.3.2 3D Integrated Circuits (3D ICs)
13.2.4 Security and Reliability in IoT Systems
13.2.4.1 Secure Boot and Trusted Execution Environments (TEE)
13.2.4.2 Hardware-Based Encryption
13.2.4.3 Fault-Tolerant Designs
13.2.5 Real-Time Data Processing and Edge Computing
13.2.5.1 Low-Latency Architectures
13.2.5.2 AI and Machine Learning on Edge Devices
13.3 Hardware Accelerators for Real-Time Processing
13.3.1 Digital Signal Processors (DSPs)
13.3.2 Graphics Processing Units (GPUs)
13.3.3 Tensor Processing Units (TPUs)
13.3.4 Quantum Computing in IoT
13.3.5 3D Integrated Circuits (3D ICs)
13.3.6 Challenges and Limitations in VLSI-Based IoT Systems
13.3.7 Advanced Security Techniques for VLSI-Based IoT Systems
13.3.8 Future Trends and Innovations in VLSI for IoT
13.9 Conclusion
References
14. Using AMF Filtering Techniques, Compression of Clinical Images for Improvement in Compression Parameters
Suman Rani and Jaibir Singh
14.1 Introduction
14.2 Methodology
14.3 Quasi Fractal, Oscillation with Hybrid Coding: Morphological Filters with Lossless Fractal Image Compression (LFIC)
14.3.1 ROI, Non-ROI Detection
14.3.2 Adaptive Threshold
14.4 Result and Discussion
14.5 Conclusion
References
15. Underwater Corrosion Detection: Utilizing Convolutional Neural Networks and Image Processing
Sai Srija Achukolu, Bhushan Vasant Nanaware, Ravindra Singh, Vaibhav Singh, Aadarsh Kumar and Harpreet Singh Bedi
15.1 Introduction
15.2 Designing of Underwater Remotely Operated Vehicle
15.2.1 Dimensions
15.2.2 Computer-Aided Design
15.3 Designing of Corrosion Detection Algorithm
15.3.1 Workflow and Algorithm
15.3.2 Corrosion Image Classification
15.4 Experimental Results
15.5 Conclusion
References
16. Metamaterial Integrated Wearable Ultrawide Bandwidth Antenna with Low Specific Absorption Rate
S. Prasad Jones Christydass, S. Suresh Kumar and Yerumbu Nandakishora
16.1 Introduction
16.2 Design of the Half-Moon-Shaped Metamaterial-Inspired Wearable Printed Antenna
16.3 Result and Discussion
16.4 SAR Analysis
16.5 Conclusion
References
17. Industry 5.0: Opportunities, Challenges, and Potential Directions for Future Research
Pankaj Kumar Keshri
17.1 Introduction
17.1.1 Industry 5.0 Technologies
17.1.2 Cloud Computing
17.1.3 Blockchain
17.1.4 Big Data Analytics
17.1.5 6G and Beyond
17.1.6 Internet of Things (IoT)
17.2 Challenges of Industry 5.0
17.2.1 Integration Complexity
17.2.2 Cybersecurity Risks
17.2.3 Data Privacy Concerns
17.2.4 Skills and Workforce Adaptation
17.2.5 High Implementation Costs
17.2.6 System Reliability and Resilience
17.2.7 Scalability Issues
17.2.8 Energy Consumption
17.3 Industry 5.0 Creative Applications
17.3.1 Intelligent Healthcare
17.3.2 Cloud Manufacturing
17.3.3 Supply Chain Management
17.3.4 Manufacturing/Production
17.3.5 Smart Education
17.3.6 Disaster Management
17.4 Potential Directions for Future Research
17.4.1 Human-Machine Collaboration
17.4.2 Ethical and Social Implications
17.4.3 Sustainability and Green Technologies
17.4.4 Personalization and Customization
17.4.5 Resilience and Adaptation
17.4.6 Interdisciplinary Applications
17.4.7 Skill Development and Workforce Transition
17.5 Conclusion
References
18. Design and Simulation of an Audio Watch to Address the Problem Faced by Elderly People
Shahid Shabir, Krishan Arora and Anshul Mahajan
18.1 Introduction
18.2 Characteristics of an Embedded System
18.3 Online Survey
References
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