Freshly Printed - allow 7 days lead
Couldn't load pickup availability
Machine Learning in Nanoelectronics
Devices, Circuits and Systems
Ashish Maurya (Edited by), Maurya (Author), Mandeep Singh (Edited by), Balwinder Raj (Edited by)
9781394336173, Wiley
Hardback, published 13 March 2026
480 pages
22.9 x 15.2 x 2.9 cm, 0.885 kg
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.
Preface xiii 1 Introduction to Machine Learning in Nanoelectronics 1 1.1 Introduction 2 1.1.1 The Need for Advanced Modeling in Nanoelectronics 2 1.1.2 Scope of Machine Learning Applications in Semiconductors 4 1.2 Evolution of Nanoelectronics: From Macroscale to Nanoscale 4 1.2.1 Moore’s Law, Transistor Scaling Challenges 4 1.2.2 Physical Scaling Limits in Nanoscale Devices 7 1.2.3 Various Nanoscale Device Technologies 9 1.2.4 Machine Learning’s Role in Overcoming Scaling Barriers 11 1.3 Machine Learning in Nanoscale Device Simulation 11 1.3.1 Traditional Simulation Techniques 12 1.3.1.1 Drift-Diffusion Model (DDM) 12 1.3.1.2 Monte Carlo (MC) Simulations 13 1.3.1.3 Non-Equilibrium Green’s Function (NEGF) Method 15 1.3.1.4 Molecular Dynamics (MD) 16 1.3.1.5 Quantum Mechanical Models: Density Functional Theory (DFT) and Tight-Binding (TB) Models 17 1.3.2 Surrogate Modeling for Device Behaviour 18 1.3.2.1 Acceleration of Quantum Simulations 18 1.3.2.2 Design Space Exploration and Optimization 19 1.3.2.3 Handling Variability and Defects 19 1.3.2.4 Transfer Learning for New Materials and Devices 19 1.3.2.5 Real-Time Parameter Tuning 20 1.4 Process Optimization in Semiconductor Manufacturing 21 1.4.1 Variability and Yield in Nanoscale Manufacturing 21 1.4.2 Real-Time Process Control with ml 22 1.4.3 Case Study: Graph-Based Yield Prediction in IC Manufacturing 24 1.4.4 Reliability, Fault Detection and Self-Heating Systems 24 1.5 Case Study: Machine Learning in Nanowire Tunnel FET Design 25 1.5.1 Device Structure 25 1.5.2 Machine Learning Approach 27 1.5.3 Design Space Exploration 28 1.5.4 Predictive Modeling 28 1.5.5 Process Variation Mitigation 28 1.6 Future Directions and Challenges 29 1.7 Conclusion 31 Summary 32 References 32 2 Machine Learning to Explore Opportunities in Quantum 43 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 References 64 3 Machine Learning (ML) and Nanotechnology to Heal Cancer: A Review 67 3.1 Introduction 69 3.2 Predictive Modelling and Machine Learning’s Application in Cancer Diagnostics 69 3.2.1 Diagnosis of Cancer 69 3.2.2 Treatment Planning 71 3.3 Customized Medical Care 72 3.3.1 Overview of Machine Learning in Healthcare 73 3.3.2 Machine Learning Applications in Cancer Therapy 74 3.3.3 Nanotechnology Applications in Cancer Therapy 76 3.4 Result and Future Perspective 77 References 79 4 Multiplexing the Brain Signals for Low Power Robust Electrode Sensing in Medical Diagnosis 89 4.1 Introduction 90 4.2 Methodology 94 4.3 Simulation Results 96 4.4 Conclusion 104 References 104 5 Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators 113 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 References 146 6 Flexible Energy Storage Devices 155 6.1 Introduction 155 6.1.1 Flexible Devices 156 6.1.2 History and Origins of Flexible Devices 156 6.1.3 The Evolution of Flexible Devices 158 6.2 Energy Storage 159 6.2.1 Energy Storage Technologies and Their History 160 6.2.1.1 Batteries 160 6.2.1.2 Supercapacitor Storage Systems (SSSs) 166 6.3 Criteria for a Device to Store Energy 167 6.3.1 The Critical Role of Energy Storage in Modern Energy Systems 168 6.4 Need of Flexible Energy Storage Devices 169 6.4.1 Advantages of Flexible Energy Storage Devices 170 6.4.2 Disadvantages of Flexible Energy Storage Devices 171 6.5 Different Structures That are Being Used in Flexible Energy Storage 172 6.5.1 Fiber Structures 173 6.5.2 Island Bridge Structure 177 6.5.3 Interdigital Structure 178 6.6 Emergence of Micro-Supercapacitors 179 6.7 Materials for Energy Storage Devices 180 6.8 Electrode Materials 180 6.8.1 Carbon-Based Electrode 181 6.8.2 Graphene‐Based Flexible Electrodes 184 6.9 Comparison Sheet of Different Materials 187 References 188 7 VLSI Design for AI Applications 197 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 7.7.1 Field Programmable Gate Array (FPGA) 215 7.7.2 Application-Specific Integrated Circuit (ASIC) 216 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 References 223 8 Ultra Low Power Adiabatic Logic Circuits at Nanometer Scale 231 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 Summary 247 References 248 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 9.1 Introduction 258 9.2 Antenna Design Equations 260 9.3 Design and Simulation 262 9.4 Conclusions 292 References 294 10 Layout Dependent Effects 307 10.1 Overview of Layout Considerations 308 10.1.1 Design Rules 308 10.2 Analog Layout Techniques 312 10.2.1 Multifinger Transistors 312 10.2.2 Symmetry 315 10.2.3 Shallow Trench Isolation Issues 319 10.3 Effects of Layout in Deep Nanoscale CMOS 320 10.3.1 Types of LDEs 321 10.4 Mismatch of Devices 326 10.4.1 Impact of Mismatch 329 10.4.2 Types of Matching 329 10.4.3 Advantages and Limitations of cc 331 References 332 11 Study of FIR Filter Hardware Architecture for Real-Time Multimedia Applications 343 11.1 Introduction 344 11.2 Digital Filtering Techniques 345 11.3 Hardware Architecture 347 11.3.1 Direct Form and Transposed Form 350 11.3.2 Hardware Analysis of an FIR Filter 353 11.3.3 Adder Logic 353 11.3.4 Multiplier Technique 354 11.3.5 Multiplier-Accumulator (MAC) Unit 354 11.3.6 FIR Filter Design without Using Multiplier 355 11.4 Simulation Setup and Results Analysis 356 11.5 Summary 359 References 360 12 Recent Trends in Deep Neural Networks and Their Hardware Implementation for Biomedical Applications 363 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 Acknowledgements 397 References 397 13 Integration with IoT for Smart Homes 409 13.1 Introduction 410 13.2 Sensors for Smart Homes 413 13.2.1 Motion Detection 413 13.2.2 Flame-Gas Detection Sensor 413 13.2.3 Toxic Gas Detection 414 13.2.4 Moisture Leak Detection 415 13.2.5 Proximity Sensors 416 13.2.6 Temperature Sensors 416 13.2.7 Humidity Sensors 417 13.2.8 Light Sensors 418 13.2.9 Smart Thermostat Sensor 418 13.2.10 Intercom/Hub 418 13.3 Connectivity Protocols for IoT Smart Homes 419 13.3.1 Zigbee 419 13.3.2 Z-Wave 419 13.3.3 Wi-Fi 420 13.3.4 Bluetooth and Bluetooth Low Energy (BLE) 420 13.3.5 MQTT (Message Queuing Telemetry Transport) 420 13.3.6 CoAP (Constrained Application Protocol) 421 13.3.7 LoRa WAN (Long Range Wide Area Network) 421 13.3.8 NFC (Near Field Communication) 421 13.3.9 Cellular(4G/5G) 422 13.4 Smart Appliances for Smart Homes 422 13.4.1 Smart Kitchen Appliances 422 13.4.2 Smart Laundry Appliances 422 13.4.3 Smart Cleaning Devices 423 13.4.4 Smart Security Devices 423 13.4.5 Smart Lighting 423 13.4.6 Smart Speaker and Hubs 423 13.4.7 Smart Energy Monitors 423 13.4.8 Integration and Automation 424 13.4.9 Benefits of Smart Devices 424 13.5 Voice Assistants 424 13.5.1 Amazon Alexa 425 13.5.2 Google Assistant 425 13.5.3 Apple Siri 425 13.5.4 Microsoft Cortana 426 13.5.5 Samsung Bixby 426 13.5.6 Raspberry Pi and Custom Assistants 426 13.6 Security and Surveillance 426 13.7 Home Healthcare System 427 13.7.1 Features for Healthcare in Smart Home 428 13.7.2 User Safety 428 13.7.3 Patient Health 429 13.7.4 Design Flexibility 430 13.7.5 Information and User Engagement 430 13.8 User Interfaces and Experiences 430 13.8.1 Mobile Apps and Dashboards 431 13.8.2 Wearable and Voice Interaction 431 13.8.3 Intuitive Design for Usability 432 13.8.4 Remote and In-Home Control Panels 432 13.9 Sustainability and Smart Homes 433 13.9.1 Energy Management 433 13.9.2 Sustainable Appliances 434 13.9.3 Smart Grids and Renewable Integration 434 13.9.4 Automated Water and Climate Control 434 13.10 Future Trends in Smart Home IoT 435 13.10.1 AI and Machine Learning 435 13.10.2 Edge Computing 436 13.10.3 5G and the Future of Connectivity 436 13.10.4 Interoperability and Universal Standards 436 13.10.5 Sustainability and Green Energy Solutions 437 13.11 Conclusions 437 References 438 About the Editors 449 Index 451
Bandi Srinivasa Rao, Rangana Bhanu Meher Srinivas, Kenguva Sai Chandar Rao, Mandeep Singh, Anil Kumar Yadav, Balwinder Raj and Tarun Chaudhary
Jyoti Khandelwal
Anshu Srivastava and Shakun Srivastava
Sarin Vijay Mythry, Dinesh N., Asha V Thalange, Chakradhar Adupa, Nanditha Krishna, Praveen Kumar Reddy and Madhuri Gummineni
Hemkant Nehete, Gaurav Verma, Amit Monga, Alok Kumar Shukla, Shailendra Yadav and Brajesh Kumar Kaushik
Tanya Singh, Akriti Dewangan, Puja Kumari, Balwinder Raj, Tarun Chaudhary Mandeep Singh and Yogesh Thakur
Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Ravi Teja, Akku Naidu and Sivaram
Jitendra Kanungo, Jitendra Raghuwanshi and Sudeb Dasgupta
Arun Raj and Durbadal Mandal
Kirti and Deepti Kakkar
Anuraj V. and Dhandapani Vaithiyanathan
Amit Monga, Hemkant Nehete, Seema Dhull, Arshid Nisar, Shailendra Yadav and Brajesh Kumar Kaushik
Akash Kumar Prajapati, Shubham Patel, Suramya Kumar Rawat, Mandeep Singh, Tarun Chaudhary and Balwinder Raj
Subject Areas: Computer science [UY]
