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Nanoelectronics for Artificial Intelligence

Edited by Raj Kumar, Balwinder Raj, Shashi Bala, and Banoth Krishna
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394386147 | Hardcover |
542 pages
Price: $225 USD

One Line Description
An indispensable roadmap for engineers and researchers leading the post-Moore’s Law revolution, this essential guide bridges the gap between material innovation and advanced nanoscale architectures, equipping you with the device-engineering strategies and leakage-mitigation techniques needed to power the next generation of AI and VLSI systems.

Description
As the semiconductor industry continues its transition beyond Moore’s Law, nanoscale device-based circuits are at the forefront, driving advancements in computational power, energy efficiency, and functionality. As the size of planar MOSFETs shrinks, there is a considerable presence of short-channel effects, prompting researchers to address these drawbacks and boost the performance of CMOS technology. The study of these devices is important for sectors whose applications in VLSI design have huge potential to bring revolutionary advancements in nanoscale devices, circuits, and systems due to their improved electronic properties for artificial intelligence. This book provides comprehensive insights into the transition from traditional planar semiconductor devices to advanced nanoscale architectures to address the challenges of nanoelectronic device-based circuits and systems. It highlights the synergy between material innovations, device engineering, techniques to reduce leakage current, and other important trade-offs, and provides an exhaustive analysis of what drives modern electronics. With a balance of foundational knowledge and forward-looking perspectives, this essential guide equips readers to navigate the challenges and opportunities presented by nanoscale integration.

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Author / Editor Details
Raj Kumar, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at Chandigarh Engineering College, Jhanjeri, Punjab, India. He has authored and co-authored more than 25 research papers in peer-reviewed international journals and conferences. His areas of interest are VLSI design, nanoelectronics, nanoscale MOSFET modeling and simulation, and physical design.

Balwinder Raj, PhD is an Associate Professor at the National Institute of Technology Jalandhar, India, with more than 15 years of teaching, research, and administrative experience. He has authored and co-authored ten books, 15 book chapters, and more than 150 research papers in peer-reviewed national and international journals and conferences. His areas of interest in research are classical and non-classical nanoscale semiconductor device modeling, nanoelectronics and their applications in hardware security, sensors and circuit design, and FinFET-based memory design.

Shashi Bala, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at Chandigarh Engineering College, Landran, Punjab, India. She has authored and co-authored more than 25 research papers in peer-reviewed international journals and conferences. Her areas of interest include microelectronics and VLSI design, nanoelectronics, nanoscale MOSFET modeling and simulation, VLSI circuit design, and memory design.

Banoth Krishna is a Professor in the Department of Electronics and Communication Engineering at Assam University, Assam, India, with more than 15 years of teaching and research experience. He has published more than 40 articles in national and international journals and conferences. His research focuses on VLSI design for biomedical applications, nanoelectronic devices, and circuits.

Table of Contents
Preface
1. From Planar to Nanoscale: The Evolution of Integrated Circuits and Their Role in Enabling AI
Anu Gupta and Afjal Ali
1.1 Introduction
1.2 The Planar Era—Foundations of IC Technology
1.3 Nanoscale Era
1.3.1 FinFETs
1.3.2 Tunnel Field-Effect Transistors (TFETs)
1.3.3 Gate-All-Around Field-Effect Transistors (GAAFETs)
1.3.4 2D Materials: Graphene and Transition Metal Dichalcogenides (TMDs)
1.4 Role of IC Technologies in AI Advancement
1.5 Conclusion
Abbreviation List
References
2. Two-Dimensional Materials-Based Devices
Kumar Prashant, Neeraj Jain, Dinesh Gupta, Kumar Manoj and Balwinder Raj
2.1 Introduction
2.1.1 Scope
2.2 Synthesis and Characterization of 2D-Based Materials
2.2.1 Synthesis Techniques
2.2.2 Characterization Methods
2.3 2D Materials: Properties
2.3.1 Electronic Properties
2.3.2 Optical Properties
2.3.3 Mechanical Properties
2.3.4 Thermal Properties
2.4 Evolution of Challenges in 2D Material Devices
2.5 Fabrication of 2D Material Devices
2.5.1 Fabrication Processes
2.5.2 Contact Engineering
2.6 Advancements in Contact Engineering for 2D Material Devices
2.6.1 Parameters
2.6.2 Key Milestones
2.6.3 Future Directions
2.7 Applications of 2D Materials-Based Devices
2.7.1 Electronics
2.7.2 Optoelectronics
2.7.3 Energy Storage
2.7.4 Sensing
2.7.5 Future Directions
2.8 Challenges and Limitations
2.9 Summary
References
3. Two-Dimensional Materials-Based Device Fabrication
Amrit Singh, Amandeep Saroa and Pratibha Sharma
3.1 Introduction
3.1.1 Overview of Two-Dimensional (2D) Materials
3.1.2 Unique Properties of 2D Materials
3.1.3 Advantages of 2D Materials for Device Applications
3.1.4 Challenges in 2D Material-Based Device Fabrication
3.2 Classification of 2D Materials
3.2.1 Graphene and Its Derivatives
3.2.2 Transition Metal Dichalcogenides (TMDs)
3.2.3 Black Phosphorus (BP)
3.2.4 Hexagonal Boron Nitride (h-BN)
3.2.5 MXenes and Other Emerging 2D Materials
3.3 Electronic and Optoelectronic Devices
3.3.1 Field-Effect Transistors (FETs) Based on 2D Materials
3.3.2 Photodetectors and Optoelectronic Sensors
3.3.3 Light-Emitting Diodes (LEDs) and Lasers
3.3.4 2D Heterostructures for Advanced Electronic Applications
3.4 Energy Storage and Conversion Devices
3.4.1 Supercapacitors and Batteries Based on 2D Materials
3.4.2 2D Material-Based Solar Cells
3.4.3 Evolution and Fuel Cell Applications
3.5 Mechanical and Flexible Electronics
3.5.1 Flexible and Wearable Electronics Based on 2D Materials
3.5.2 Strain and Pressure Sensors
3.5.3 Nanoelectromechanical Systems (NEMS)
3.6 Quantum and Spintronic Devices
3.6.1 2D Materials for Quantum Computing
3.6.2 Spintronics and Valleytronics in 2D Systems
3.6.3 Topological Insulators and Exotic Quantum States
3.7 Future Perspectives and Challenges
3.7.1 Scalability and Industrial Applications
3.7.2 Environmental and Safety Concerns
3.7.3 Emerging Trends and Future Research Directions
3.8 Conclusion
3.8.1 Summary of Key Findings
3.8.2 Outlook on the Future of 2D Material-Based Devices
References
4. Advancements in Nanoelectronics
S. Shafiulla Basha and K. Mahaboob Basha
4.1 Introduction
4.2 IC Development Over Time: From Planar to Nanoscale
4.3 Fundamentals of Nanoelectronic Device Semiconductor Physics
4.4 Artificial Intelligence Device Architectures at the Nanoscale
4.5 Nanowire, Nanotube, Nanosheet, and FinFET Transistors
4.6 Comparing FinFET, Nanowire, CNT, and Nanosheet Transistors
4.7 Devices Based on Two-Dimensional Materials
4.8 Digital and Analog Circuit Junctionless Transistors
4.9 Nanoscale Device-Based VLSI Circuits and Nanosheet FETs
4.10 Tunnel Field-Effect Transistors (TETs) for Memory Design
in AI
4.11 Carbon Nanotubes and Spintronic Devices
4.12 Memristors and Other Emerging Devices
4.13 Machine Learning for IC Design and Verification
4.14 Overview of Neuromorphic and Quantum Computing: Devices and Architectures
4.15 Emerging Frontiers and Implications in Nanoelectronics
4.16 Societal Impact and Future Outlook
4.17 Challenges and Considerations
4.18 Summary: The Role of Nanoelectronics in Advancing AI Hardware
4.19 Conclusion and Future Scope
References
5. Basics of Semiconductor Physics for Nanoelectronic Devices
Sarabjeet Kaur, Avtar Singh and Vidushi Karol
5.1 Introduction to Nanoelectronics
5.2 Crystal Structure and Energy Bands
5.3 Carrier Statistics
5.4 Carrier Transport Phenomena
5.5 P–N Junctions
5.6 Metal–Semiconductor Junctions
5.7 Transistors
5.8 Quantum Confinement Effects
5.9 Nanoelectronic Materials
5.10 Challenges and Future Directions
5.11 Conclusions
5.12 Summary
References
6. 2D Material-Based Heterostructure Devices
Lisha, Neelu Mahajan, Bhupinder Kaur, Shashi Bala and Vidushi Karol
6.1 Introduction
6.2 Types of Two-Dimensional Materials
6.3 Synthesis and Fabrication Techniques
6.4 Electronic Devices Based on 2D Materials
6.5 Optoelectronic Devices
6.6 Energy Storage and Conversion Devices
6.7 Flexible and Wearable Electronics
6.8 Challenges and Future Perspectives
6.9 Conclusions
6.10 Summary
References
7. Synthesis of 2D Materials Devices and Applications
Manmeet Singh, Misha Thakur, Ashish Kumar, Ashwani K. Sharma and Raj Kumar
7.1 Introduction
7.1.1 Historical Development of 2D Materials
7.1.2 Transition Metal Dichalcogenides (TMDs) (e.g., MoS₂, WS₂, WSe₂)
7.1.3 Hexagonal Boron Nitride (h-BN)
7.1.4 Black Phosphorus (BP)
7.1.5 MXenes
7.2 Classification of 2D Materials
7.2.1 Conductors (e.g., Graphene, MXenes)
7.2.2 Semiconductors (e.g., Transition Metal Dichalcogenides (TMDs), Black Phosphorus)
7.2.3 Insulators (e.g., Hexagonal Boron Nitride (h-BN))
7.3 Unique Features of 2D Materials
7.3.1 Atomic Thickness
7.3.2 Mechanical Flexibility
7.3.3 High Surface-to-Volume Ratio
7.3.4 Tunable Band Gap
7.3.5 van der Waals Bonding
7.3.6 Surface Reactivity
7.4 Importance in the Post-CMOS Era
7.4.1 Synthesis Techniques for Device-Grade 2D Materials
7.4.2 Mechanical Exfoliation
7.4.3 Chemical Vapor Deposition (CVD)
7.4.4 Liquid-Phase Exfoliation
7.4.5 Molecular Beam Epitaxy (MBE)
7.4.6 Solution-Based Synthesis
7.5 Key Considerations in Selecting Synthesis Techniques for 2D Materials
7.5.1 Primary Application
7.5.2 Material Classification and Structural Characteristics
7.5.3 Specification of Layer and Size Control
7.5.4 Industrial Viability and Scale-Up Potential
7.5.5 Environmental Protection and Safety Measures
7.6 Characterization Techniques
7.6.1 Framework and Morphological Characterization
7.6.2 Spectroscopic Characterization
7.6.3 Elemental and Chemical Composition Analysis
7.6.4 Light-Based and Thermal Characterization
7.7 Device Construction and Hybridization with 2D Materials
7.7.1 Optimization and Preparation of Substrate
7.7.2 Transfer Procedures
7.7.3 Substrate Patterning via Lithography
7.7.4 Interface Engineering and Contact Formation
7.7.5 Integration of Heterostructure and Multiple Layers
7.7.6 Environmental Stability and Encapsulation
7.7.7 Scalable Platform and CMOS Integration
7.8 Incorporation of 2D Materials in Device Technologies
7.8.1 Field-Effect Transistors
7.8.2 Photo-Sensing and Photovoltaic Devices
7.8.3 Sensing Devices
7.8.4 Memory Circuits and Logic Devices
7.8.5 Storage and Conversion of Energy
7.8.6 Smart Wearable Electronics
7.8.7 Quantum-Enabled and Spin-Based Electronic Devices
7.9 Important Challenges in Using 2D Materials
7.9.1 Material Preparation
7.9.2 Device Manufacturing
7.9.3 Environmental Conditions
7.9.4 Analyses and Standards
7.10 Upcoming Developments and Commercial Viability
7.10.1 Outlook on Scale-Up and Manufacturing Prospects
7.10.2 Market Opportunities and Barriers
7.10.3 Pioneering Research Topics and Multidisciplinary Opportunities
7.10.4 Sustainable Development and Ecological Concerns
7.10.5 Key Requirements for Achieving Commercial Success
7.10.6 Integrated Collaboration and Policy Facilitation
7.11 Executive Summary
References
8. Negative Capacitance Gate-All-Around Nanosheet FET for Low Power Application
Raj Kumar, Shashi Bala, Vidushi Karol, Ankur Garg and Rekha Devi
8.1 Introduction
8.2 Structure and Operation of Nanosheet FETs
8.3 Negative Capacitance (NC) Effect in FETs
8.4 Gate-All-Around FETs (GAA FETs)
8.5 Negative Capacitance Gate-All-Around FETs (NC GAA FET)
8.6 NC Nanosheet FET Device Simulation Results
8.7 Conclusions
8.8 Summary
References
9. Two-Dimensional Material-Based Semiconductor Devices
Ankur Garg, Rajeev Kumar, Baljinder Kaur, Raj Kumar, Parveen Kumar and Sandeep Kumar
9.1 Introduction
9.2 Device Structure
9.3 2D Material-Based Devices
9.4 Summary and Conclusion
References
10. Converging Paradigms: An Overview of Neuromorphic and Quantum Computing Architectures and Devices
Shivani Goyal, Deeksha Verma, Kamalpreet Kaur Dhaliwal and Raj Kumar
10.1 Introduction
10.2 Theoretical Background
10.3 Digital and Analog Paradigms in Quantum Neuromorphic
Computing
10.4 Recent Architectures and Devices in Neuromorphic and Quantum Neuromorphic Computing
10.5 Conclusion and Future Scope
References
11. Recent Advances and Challenges in STT-MRAM for Next-Generation MRAM
Pillem Ramesh, G. Erna and Atul S. M. Tripathi
11.1 Introduction
11.2 STT-MRAM Structure and Operation
11.3 Recent Advances and Challenges in STT-MRAM
11.4 Applications
11.5 Future Directions
11.6 Conclusion
References
12. Carbon Nanotubes and Spintronic Devices
Avtar Singh, Sachin Mohal, Sarabjeet Kaur, Vidushi Karol and Neeraj Kamboj
12.1 Introduction
12.2 Carbon Nanotubes: Structure and Properties
12.2.1 Types of Carbon Nanotubes
12.2.2 Electrical and Magnetic Characteristics Relevant to Spintronics
12.2.2.1 Electrical Characteristics in Spintronics
12.2.2.2 Magnetic Characteristics in Spintronics
12.3 Fundamentals of Spintronics
12.3.1 Spin as a Degree of Freedom
12.3.2 Density Functional Theory (DFT) Studies
12.3.3 Molecular Dynamics Simulations
12.3.4 Modelling of Spin-Dependent Transport
12.4 Spin Transport in Carbon Nanotubes
12.4.1 Spin Injection and Detection
12.4.2 Spin Coherence and Relaxation
12.4.3 Magnetoresistance in CNTs
12.5 CNT-Based Spintronic Devices
12.5.1 Ferromagnetic Contacts and Spin Valves
12.5.2 Newer Approaches to CNTs and Their Functions for Future Spintronic Use
12.5.3 DNA-Wrapped CNTs
12.5.4 Chemical Functionalization and Defect Engineering
12.6 Fabrication Techniques
12.6.1 Carbon Nanotube Synthesis and Purification
12.6.2 Device Fabrication Methods
12.6.3 Electrode Deposition and Contact Engineering
12.6.4 Characterization and Optimization
12.7 Recent Advances and Applications
12.8 Challenges and Future Directions
12.8.1 Contact Resistance and Interface Properties
12.8.2 CNT Synthesis and Placement
12.8.3 Material Quality and Reproducibility
12.9 Applications and Future Directions
12.9.1 High-Frequency and Low-Power Electronics
12.9.2 Quantum Computing
12.9.3 Sensors and Novel Applications
12.10 Conclusion
12.11 Summary
References
13. Design and Simulation of High-Speed CMOS Comparators for ADC Applications
Nilkamal Sharma, Farhana Akhtar, Xavinian Raplang Thabah and Banoth Krishna
13.1 Introduction
13.2 Literature Review
13.3 Design Methodology
13.4 Comparator Basics
13.5 Comparator Topologies
13.6 Design and Implementation
13.7 Simulation Results and Discussion
Discussion
13.8 Summary
13.9 Conclusion
References
14. Comparison Analysis of Two-Stage, Folded Cascode, and Telescopic Differential Amplifiers in 180-nm CMOS Technology
Gourab Ghose, Swaraj Malakar, Rishika Pasi and Banoth Krishna
14.1 Introduction
14.2 Literature Review
14.3 Design Methodology
14.3.1 Two-Stage Differential Amplifier
14.3.2 Telescopic Differential Amplifier
14.3.3 Folded Cascode Differential Amplifier
14.4 Design Challenges and Optimization
14.5 Simulation Setup
14.6 Discussion and Comparative Analysis
14.7 Applications in Biomedical Systems
14.8 Noise and Linearity Analysis
14.9 Circuit Diagram
14.10 Design Parameters
14.11 Layout Considerations
14.12 Cadence Schematic
14.13 Simulation Result
14.14 Simulation and Performance Comparison
14.15 Future Scope
14.16 Acknowledgement
14.17 Conclusion
References
15. High-Performance CMOS Operational Amplifier Design Using CMOS FET, FinFET and GAA FET for Industrial and Biomedical Applications
Kottu Deena and Bangaru Ramana Kumar
15.1 Introduction of CMOS Operational Amplifier
15.2 The Ubiquity of Operational Amplifiers
15.3 Importance in Industrial Applications
15.4 Significance in Biomedical Applications
15.5 Challenges in CMOS Op-Amp Design
15.6 CMOS Technology for Op-Amp Design
15.7 Objectives
15.7.1 Proposed Op-Amp Architecture and Design
15.7.1.1 Architecture and Design
15.8 Design Considerations and Trade-Offs
15.9 CMOS Technology Scaling
15.9.1 Compensation Techniques
15.9.2 Applications
15.10 Optimization Algorithms
15.11 Simulation Results and Performance Analysis
15.12 Application in Biomedical Signal Acquisition
15.12.1 Clinical Diagnostics and Monitoring
15.13 Challenges and Future Directions
15.14 FinFET
15.14.1 What is FinFET
15.14.2 Why Use a FinFET Device in Place of MOSFET?
15.14.3 Some of the Problems That Arise Due to Short Channel Effect
15.14.4 Basic Structure of FinFET
15.14.4.1 Computing FinFET Transistor Width
15.14.5 Difference between FinFET and MOSFET
15.14.6 Classification of FinFET
15.14.7 Classification of FinFET Based on Type of Substrate
15.14.8 Advantages and Disadvantages of FinFET
15.14.8.1 Disadvantages of FinFET
15.14.9 Application of FinFET
15.15 Gate-All-Around FET (GAA FET)
15.15.1 Nanosheet GAA
15.15.1.1 Nanowire GAA
15.15.2 GAA Operation
15.15.2.1 Electrostatic Control Current Steering
15.15.3 Fabrication Techniques for Gate-All-Around Transistors
15.15.4 Challenges in GAA Fabrication
15.15.5 Different Fabrications Techniques for Gate-All-Around
15.15.6 Material Considerations
15.15.7 Lithography and Etching Techniques
15.15.8 Electrical Characteristics of Gate-All-Around Transistors
15.15.8.1 Current–Voltage (I-V) Characteristics
15.15.8.2 Threshold Voltage and Its Dependence on Various Parameters
15.15.9 Subthreshold Wing and Its Impact on Power Consumption
15.15.10 Short-Channel Effects and Their Mitigation in GAA Transistors
15.15.11 Performance and Advantages of Gate-All-Around Transistors
15.15.11.1 Higher Drive Current and Improved Performance at Lower Voltages
15.15.11.2 Reduced Leakage Current and Improved Power Efficiency
15.15.11.3 Enhanced Scalability and Potential for Further Miniaturization
15.15.12 Applications and Future Prospects of Gate-All-Around Transistors
15.15.13 Challenges and Future Research Directions
15.15.14 Comparison with Other Transistor Architectures
15.16 Conclusion
References
16. Recent Advancements in Brain Tumor Detection Using Deep
Learning and GAN
Saryu Verma and Jatin Arora
16.1 Introduction
16.1.1 The Role of Deep Learning in Medical Imaging
16.1.2 CNN-Based Brain Imaging and Segmentation
16.1.3 Early Detection of Brain Tumor Recurrence
16.1.4 Generative Models for Tumor Detection and Augmentation
16.1.5 Transfer Learning for Brain Tumor Detection
16.2 Revolutionizing Brain Tumor Discovery through the Use of Deep Learning
16.3 MRI-Based Tumor Segmentation Using Deep Learning
16.4 Enhancing Brain Tumor Detection with AR/VR Technology
16.5 Multi-Class Brain Tumor Detection Using CNN and Traditional Classifiers
16.6 Research Findings and Discussion
16.7 Applications and Profitable Implications on Society
16.8 Key Issues and Limitations
16.9 Conclusion
16.10 Emerging and Future Directions
Summary
References
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Nanoelectronics for Artificial Intelligence