Overview

Bridge the gap between advanced algorithms and hardware innovation with this essential book, which details how machine learning is being used to overcome challenges in nanoelectronics while laying the critical groundwork for the future of neuromorphic computing hardware. New techniques for obtaining insights from enormous amounts of data and efficiently acquiring smaller data sets are provided by recent developments in machine learning. Researchers in nanoscience and nanoelectronics are experimenting with these tools to tackle challenges across many fields. Nanoscience and nanoelectronics not only advance machine learning but also lay the groundwork for neuromorphic computing hardware to broaden machine learning algorithm implementation. This book is a collection of possibilities for machine learning in nanoelectronics, semiconductor devices, and based circuits. With an easy-to-understand approach, this book explores the latest in machine learning in nanoelectronics materials and nanoscale devices through insights and analysis of recent developments in nanoelectronics.

Full Product Details

Author:   Ashish Maurya (Bennett University) ,  Mandeep Singh (Indian Institute of Information Technology, India) ,  Balwinder Raj (National Institute of Technology Jalandhar, India)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-Scrivener
ISBN:  

9781394336173

ISBN 10:   1394336179
Pages:   480
Publication Date:   13 March 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Preface xiii 1 Introduction to Machine Learning in Nanoelectronics 1 Bandi Srinivasa Rao, Rangana Bhanu Meher Srinivas, Kenguva Sai Chandar Rao, Mandeep Singh, Anil Kumar Yadav, Balwinder Raj and Tarun Chaudhary 1.1 Introduction 2 1.2 Evolution of Nanoelectronics: From Macroscale to Nanoscale 4 1.3 Machine Learning in Nanoscale Device Simulation 11 1.4 Process Optimization in Semiconductor Manufacturing 21 1.5 Case Study: Machine Learning in Nanowire Tunnel FET Design 25 1.6 Future Directions and Challenges 29 1.7 Conclusion 31 2 Machine Learning to Explore Opportunities in Quantum 43 Jyoti Khandelwal 2.1 Introduction to Quantum Opportunities 44 2.2 Understanding Quantum Data 46 2.3 Machine Learning Techniques for Quantum Applications 49 2.4 Case Studies and Applications 57 2.5 Tools and Frameworks for Implementation 60 2.6 Challenges and Opportunities in QML 63 2.7 Conclusion 63 3 Machine Learning (ML) and Nanotechnology to Heal Cancer: A Review 67 Anshu Srivastava and Shakun Srivastava 3.1 Introduction 69 3.2 Predictive Modelling and Machine Learning's Application in Cancer Diagnostics 69 3.3 Customized Medical Care 72 3.4 Result and Future Perspective 77 4 Multiplexing the Brain Signals for Low Power Robust Electrode Sensing in Medical Diagnosis 89 Sarin Vijay Mythry, Dinesh N., Asha V Thalange, Chakradhar Adupa, Nanditha Krishna, Praveen Kumar Reddy and Madhuri Gummineni 4.1 Introduction 90 4.2 Methodology 94 4.3 Simulation Results 96 4.4 Conclusion 104 5 Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators 113 Hemkant Nehete, Gaurav Verma, Amit Monga, Alok Kumar Shukla, Shailendra Yadav and Brajesh Kumar Kaushik 5.1 Introduction 114 5.2 Computational Complexities of Convolutional Neural Networks 115 5.3 Evolution of CNN Accelerators 119 5.4 Model Compression Approaches 121 5.5 Hardware Optimization Techniques 124 5.6 Design Space Exploration 129 5.7 Hardware Platforms for Implementing CNNs 134 5.8 Sparse Neural Networks 141 5.9 Future Scope and Summary 145 6 Flexible Energy Storage Devices 155 Tanya Singh, Akriti Dewangan, Puja Kumari, Balwinder Raj, Tarun Chaudhary Mandeep Singh and Yogesh Thakur 6.1 Introduction 155 6.2 Energy Storage 159 6.3 Criteria for a Device to Store Energy 167 6.4 Need of Flexible Energy Storage Devices 169 6.5 Different Structures That are Being Used in Flexible Energy Storage 172 6.6 Emergence of Micro-Supercapacitors 179 6.7 Materials for Energy Storage Devices 1806.8 Electrode Materials 180 6.9 Comparison Sheet of Different Materials 187 7 VLSI Design for AI Applications 197 Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Ravi Teja, Akku Naidu and Sivaram 7.1 Introduction 198 7.2 Specialized Neural Networks Accelerators 201 7.3 Memory Hierarchy Optimization 204 7.4 High Speed Interconnects 208 7.5 Power Optimization 211 7.6 Scalability 213 7.7 Key Components of VLSI Design for AI 214 7.8 Accelerating Chip Design Using ML 217 7.9 Future Trends in VLSI Design for AI 219 7.10 Industrial Application of VLSI Design 221 8 Ultra Low Power Adiabatic Logic Circuits at Nanometer Scale 231 Jitendra Kanungo, Jitendra Raghuwanshi and Sudeb Dasgupta 8.1 Introduction 232 8.2 Adiabatic Charging Principle 232 8.3 Adiabatic Logic Family 234 8.4 Comparative Simulation Results 236 8.5 Key Challenges 236 8.6 Comparative Analysis of Energy Recovery Logic and Conventional CMOS Logic 240 9 High-Frequency Laminate Material-Based Antennas: Deploying Bridge-Coupled Antenna Arrays for mm Wave 5G and IoT V2X Telemetry Systems in Smart Cities 257 Arun Raj and Durbadal Mandal 9.1 Introduction 258 9.2 Antenna Design Equations 260 9.3 Design and Simulation 262 9.4 Conclusions 292 10 Layout Dependent Effects 307 Kirti and Deepti Kakkar 10.1 Overview of Layout Considerations 308 10.2 Analog Layout Techniques 312 10.3 Effects of Layout in Deep Nanoscale CMOS 320 10.4 Mismatch of Devices 326 11 Study of FIR Filter Hardware Architecture for Real-Time Multimedia Applications 343 Anuraj V. and Dhandapani Vaithiyanathan 11.1 Introduction 344 11.2 Digital Filtering Techniques 345 11.3 Hardware Architecture 347 11.4 Simulation Setup and Results Analysis 356 11.5 Summary 359 12 Recent Trends in Deep Neural Networks and Their Hardware Implementation for Biomedical Applications 363 Amit Monga, Hemkant Nehete, Seema Dhull, Arshid Nisar, Shailendra Yadav and Brajesh Kumar Kaushik 12.1 Introduction 364 12.2 Neural Network Architectures 365 12.3 Deep Learning Algorithms for Medical Images 373 12.4 Recent Trends in Hardware Architectures of DNN 386 12.5 Challenges and Opportunities 393 12.6 Summary 396 13 Integration with IoT for Smart Homes 409 Akash Kumar Prajapati, Shubham Patel, Suramya Kumar Rawat, Mandeep Singh, Tarun Chaudhary and Balwinder Raj 13.1 Introduction 410 13.2 Sensors for Smart Homes 413 13.3 Connectivity Protocols for IoT Smart Homes 419 13.4 Smart Appliances for Smart Homes 422 13.5 Voice Assistants 424 13.6 Security and Surveillance 426 13.7 Home Healthcare System 427 13.8 User Interfaces and Experiences 430 13.9 Sustainability and Smart Homes 433 13.10 Future Trends in Smart Home IoT 435 13.11 Conclusions 437 References 438 About the Editors 449 Index 451

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Author Information

Ashish Maurya, PhD is an Assistant Professor in the Electronics and Communication Engineering Department and Assistant Dean of Research and Development at the Kanpur Institute of Technology. He has published nine journal articles and seven international conference proceedings. His current research interests include machine learning in semiconductor physics, nanoelectronics, and emerging semiconductor materials and their applications in various analog and digital circuits. Mandeep Singh is a Professor in the Electronics and Communication Engineering Department at the Indian Institute of Information Technology. He has published three books, five book chapters, and various research papers in international journals. His areas of research include semiconductor device modeling, memory design, and low-power VLSI design. Balwinder Raj, PhD is an Associate Professor at the National Institute of Technology Jalandhar. 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 include classical and non-classical nanoscale semiconductor device modeling, nanoelectronics, FinFET-based memory design, and low-power VLSI design.

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