Machine Learning in Nanoelectronics

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.

• Preface xiii1 Introduction to Machine Learning in Nanoelectronics 1Bandi Srinivasa Rao, Rangana Bhanu Meher Srinivas, Kenguva Sai Chandar Rao, Mandeep Singh, Anil Kumar Yadav, Balwinder Raj and Tarun Chaudhary1.1 Introduction 21.1.1 The Need for Advanced Modeling in Nanoelectronics 21.1.2 Scope of Machine Learning Applications in Semiconductors 41.2 Evolution of Nanoelectronics: From Macroscale to Nanoscale 41.2.1 Moore’s Law, Transistor Scaling Challenges 41.2.2 Physical Scaling Limits in Nanoscale Devices 71.2.3 Various Nanoscale Device Technologies 91.2.4 Machine Learning’s Role in Overcoming Scaling Barriers 111.3 Machine Learning in Nanoscale Device Simulation 111.3.1 Traditional Simulation Techniques 121.3.1.1 Drift-Diffusion Model (DDM) 121.3.1.2 Monte Carlo (MC) Simulations 131.3.1.3 Non-Equilibrium Green’s Function (NEGF) Method 151.3.1.4 Molecular Dynamics (MD) 161.3.1.5 Quantum Mechanical Models: Density Functional Theory (DFT) and Tight-Binding (TB) Models 171.3.2 Surrogate Modeling for Device Behaviour 181.3.2.1 Acceleration of Quantum Simulations 181.3.2.2 Design Space Exploration and Optimization 191.3.2.3 Handling Variability and Defects 191.3.2.4 Transfer Learning for New Materials and Devices 191.3.2.5 Real-Time Parameter Tuning 201.4 Process Optimization in Semiconductor Manufacturing 211.4.1 Variability and Yield in Nanoscale Manufacturing 211.4.2 Real-Time Process Control with ml 221.4.3 Case Study: Graph-Based Yield Prediction in IC Manufacturing 241.4.4 Reliability, Fault Detection and Self-Heating Systems 241.5 Case Study: Machine Learning in Nanowire Tunnel FET Design 251.5.1 Device Structure 251.5.2 Machine Learning Approach 271.5.3 Design Space Exploration 281.5.4 Predictive Modeling 281.5.5 Process Variation Mitigation 281.6 Future Directions and Challenges 291.7 Conclusion 31Summary 32References 322 Machine Learning to Explore Opportunities in Quantum 43Jyoti Khandelwal2.1 Introduction to Quantum Opportunities 442.2 Understanding Quantum Data 462.3 Machine Learning Techniques for Quantum Applications 492.4 Case Studies and Applications 572.5 Tools and Frameworks for Implementation 602.6 Challenges and Opportunities in QML 632.7 Conclusion 63References 643 Machine Learning (ML) and Nanotechnology to Heal Cancer: A Review 67Anshu Srivastava and Shakun Srivastava3.1 Introduction 693.2 Predictive Modelling and Machine Learning’s Application in Cancer Diagnostics 693.2.1 Diagnosis of Cancer 693.2.2 Treatment Planning 713.3 Customized Medical Care 723.3.1 Overview of Machine Learning in Healthcare 733.3.2 Machine Learning Applications in Cancer Therapy 743.3.3 Nanotechnology Applications in Cancer Therapy 763.4 Result and Future Perspective 77References 794 Multiplexing the Brain Signals for Low Power Robust Electrode Sensing in Medical Diagnosis 89Sarin Vijay Mythry, Dinesh N., Asha V Thalange, Chakradhar Adupa, Nanditha Krishna, Praveen Kumar Reddy and Madhuri Gummineni4.1 Introduction 904.2 Methodology 944.3 Simulation Results 964.4 Conclusion 104References 1045 Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators 113Hemkant Nehete, Gaurav Verma, Amit Monga, Alok Kumar Shukla, Shailendra Yadav and Brajesh Kumar Kaushik5.1 Introduction 1145.2 Computational Complexities of Convolutional Neural Networks 1155.3 Evolution of CNN Accelerators 1195.4 Model Compression Approaches 1215.5 Hardware Optimization Techniques 1245.6 Design Space Exploration 1295.7 Hardware Platforms for Implementing CNNs 1345.8 Sparse Neural Networks 1415.9 Future Scope and Summary 145References 1466 Flexible Energy Storage Devices 155Tanya Singh, Akriti Dewangan, Puja Kumari, Balwinder Raj, Tarun Chaudhary Mandeep Singh and Yogesh Thakur6.1 Introduction 1556.1.1 Flexible Devices 1566.1.2 History and Origins of Flexible Devices 1566.1.3 The Evolution of Flexible Devices 1586.2 Energy Storage 1596.2.1 Energy Storage Technologies and Their History 1606.2.1.1 Batteries 1606.2.1.2 Supercapacitor Storage Systems (SSSs) 1666.3 Criteria for a Device to Store Energy 1676.3.1 The Critical Role of Energy Storage in Modern Energy Systems 1686.4 Need of Flexible Energy Storage Devices 1696.4.1 Advantages of Flexible Energy Storage Devices 1706.4.2 Disadvantages of Flexible Energy Storage Devices 1716.5 Different Structures That are Being Used in Flexible Energy Storage 1726.5.1 Fiber Structures 1736.5.2 Island Bridge Structure 1776.5.3 Interdigital Structure 1786.6 Emergence of Micro-Supercapacitors 1796.7 Materials for Energy Storage Devices 1806.8 Electrode Materials 1806.8.1 Carbon-Based Electrode 1816.8.2 Graphene‐Based Flexible Electrodes 1846.9 Comparison Sheet of Different Materials 187References 1887 VLSI Design for AI Applications 197Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Ravi Teja, Akku Naidu and Sivaram7.1 Introduction 1987.2 Specialized Neural Networks Accelerators 2017.3 Memory Hierarchy Optimization 2047.4 High Speed Interconnects 2087.5 Power Optimization 2117.6 Scalability 2137.7 Key Components of VLSI Design for AI 2147.7.1 Field Programmable Gate Array (FPGA) 2157.7.2 Application-Specific Integrated Circuit (ASIC) 2167.8 Accelerating Chip Design Using ml 2177.9 Future Trends in VLSI Design for AI 2197.10 Industrial Application of VLSI Design 221References 2238 Ultra Low Power Adiabatic Logic Circuits at Nanometer Scale 231Jitendra Kanungo, Jitendra Raghuwanshi and Sudeb Dasgupta8.1 Introduction 2328.2 Adiabatic Charging Principle 2328.3 Adiabatic Logic Family 2348.4 Comparative Simulation Results 2368.5 Key Challenges 2368.6 Comparative Analysis of Energy Recovery Logic and Conventional CMOS Logic 240Summary 247References 2489 High-Frequency Laminate Material-Based Antennas: Deploying Bridge-Coupled Antenna Arrays for mm Wave 5G and IoT V2X Telemetry Systems in Smart Cities 257Arun Raj and Durbadal Mandal9.1 Introduction 2589.2 Antenna Design Equations 2609.3 Design and Simulation 2629.4 Conclusions 292References 29410 Layout Dependent Effects 307Kirti and Deepti Kakkar10.1 Overview of Layout Considerations 30810.1.1 Design Rules 30810.2 Analog Layout Techniques 31210.2.1 Multifinger Transistors 31210.2.2 Symmetry 31510.2.3 Shallow Trench Isolation Issues 31910.3 Effects of Layout in Deep Nanoscale CMOS 32010.3.1 Types of LDEs 32110.4 Mismatch of Devices 32610.4.1 Impact of Mismatch 32910.4.2 Types of Matching 32910.4.3 Advantages and Limitations of cc 331References 33211 Study of FIR Filter Hardware Architecture for Real-Time Multimedia Applications 343Anuraj V. and Dhandapani Vaithiyanathan11.1 Introduction 34411.2 Digital Filtering Techniques 34511.3 Hardware Architecture 34711.3.1 Direct Form and Transposed Form 35011.3.2 Hardware Analysis of an FIR Filter 35311.3.3 Adder Logic 35311.3.4 Multiplier Technique 35411.3.5 Multiplier-Accumulator (MAC) Unit 35411.3.6 FIR Filter Design without Using Multiplier 35511.4 Simulation Setup and Results Analysis 35611.5 Summary 359References 36012 Recent Trends in Deep Neural Networks and Their Hardware Implementation for Biomedical Applications 363Amit Monga, Hemkant Nehete, Seema Dhull, Arshid Nisar, Shailendra Yadav and Brajesh Kumar Kaushik12.1 Introduction 36412.2 Neural Network Architectures 36512.3 Deep Learning Algorithms for Medical Images 37312.4 Recent Trends in Hardware Architectures of DNN 38612.5 Challenges and Opportunities 39312.6 Summary 396Acknowledgements 397References 39713 Integration with IoT for Smart Homes 409Akash Kumar Prajapati, Shubham Patel, Suramya Kumar Rawat, Mandeep Singh, Tarun Chaudhary and Balwinder Raj13.1 Introduction 41013.2 Sensors for Smart Homes 41313.2.1 Motion Detection 41313.2.2 Flame-Gas Detection Sensor 41313.2.3 Toxic Gas Detection 41413.2.4 Moisture Leak Detection 41513.2.5 Proximity Sensors 41613.2.6 Temperature Sensors 41613.2.7 Humidity Sensors 41713.2.8 Light Sensors 41813.2.9 Smart Thermostat Sensor 41813.2.10 Intercom/Hub 41813.3 Connectivity Protocols for IoT Smart Homes 41913.3.1 Zigbee 41913.3.2 Z-Wave 41913.3.3 Wi-Fi 42013.3.4 Bluetooth and Bluetooth Low Energy (BLE) 42013.3.5 MQTT (Message Queuing Telemetry Transport) 42013.3.6 CoAP (Constrained Application Protocol) 42113.3.7 LoRa WAN (Long Range Wide Area Network) 42113.3.8 NFC (Near Field Communication) 42113.3.9 Cellular(4G/5G) 42213.4 Smart Appliances for Smart Homes 42213.4.1 Smart Kitchen Appliances 42213.4.2 Smart Laundry Appliances 42213.4.3 Smart Cleaning Devices 42313.4.4 Smart Security Devices 42313.4.5 Smart Lighting 42313.4.6 Smart Speaker and Hubs 42313.4.7 Smart Energy Monitors 42313.4.8 Integration and Automation 42413.4.9 Benefits of Smart Devices 42413.5 Voice Assistants 42413.5.1 Amazon Alexa 42513.5.2 Google Assistant 42513.5.3 Apple Siri 42513.5.4 Microsoft Cortana 42613.5.5 Samsung Bixby 42613.5.6 Raspberry Pi and Custom Assistants 42613.6 Security and Surveillance 42613.7 Home Healthcare System 42713.7.1 Features for Healthcare in Smart Home 42813.7.2 User Safety 42813.7.3 Patient Health 42913.7.4 Design Flexibility 43013.7.5 Information and User Engagement 43013.8 User Interfaces and Experiences 43013.8.1 Mobile Apps and Dashboards 43113.8.2 Wearable and Voice Interaction 43113.8.3 Intuitive Design for Usability 43213.8.4 Remote and In-Home Control Panels 43213.9 Sustainability and Smart Homes 43313.9.1 Energy Management 43313.9.2 Sustainable Appliances 43413.9.3 Smart Grids and Renewable Integration 43413.9.4 Automated Water and Climate Control 43413.10 Future Trends in Smart Home IoT 43513.10.1 AI and Machine Learning 43513.10.2 Edge Computing 43613.10.3 5G and the Future of Connectivity 43613.10.4 Interoperability and Universal Standards 43613.10.5 Sustainability and Green Energy Solutions 43713.11 Conclusions 437References 438About the Editors 449Index 451