Machine Learning in Nanoelectronics
Description
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.
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Persons
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.
Content
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
1.1.1 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 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
References 64
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
3.2.1 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 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
References 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
References 146
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
6.1.1 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 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
7.7.1 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 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
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 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
References 294
10 Layout Dependent Effects 307 Kirti and Deepti Kakkar
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 Anuraj V. and Dhandapani Vaithiyanathan
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 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
Acknowledgements 397
References 397
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
13.2.1 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
