VLSI Design for Artificial Intelligence and Machine Learning Applications

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 eight books, 15 book chapters, and more than 150 research papers. His research interests include nanoscale semiconductor device modeling, nanoelectronics, hardware security, sensors, circuit design, and FinFET-based memory design. Koushik Guha, PhD is an Associate Professor and Head of the Department of Electronics and Communication Engineering at the National Institute of Technology, Silchar, India. He has published more than 200 journal and conference papers, authored over 35 book chapters, and two books. His research interests include MEMS, RF MEMS, BIO-MEMS, smart sensors for IoT, and VLSI circuit design and optimization. Shiromani Balmukund Rahi, PhD is an Assistant Professor in the School of Information and Communication Technology at Gautam Buddha University, Greater Noida, India. He has published 25 research articles, two conference proceedings, 25 book chapters, and seven books. His research focuses on low-power VLSI design, quantum-dot cellular automata, non-volatile memory devices, logic-in-memory computing, and neuromorphic computing. Nehru Kandasamy, PhD is a Professor in the Department of Electronics and Communication Engineering at the Madanapalle Institute of Technology and Science, India. He has published numerous peer-reviewed articles and serves as a reviewer for more than 15 journals. His research interests include low-power VLSI design, quantum-dot cellular automata, non-volatile memory devices, logic-in-memory computing, and neuromorphic computing.

• Preface xxiii1 2D Nanomaterials for VLSI Devices 1Sukanya Ghosh1.1 Introduction 21.2 Recent Progress in 2D Nanomaterials 61.3 Optimizing FETs toward Theoretical Performance Limits 91.4 2D Materials in Heterogeneously Integrated VLSI Devices 121.5 2D Material Sensors 191.6 Conclusion and Future Scope 232 Nano-MOSFETs, Double-Gate MOSFET, FinFET, and TFET 31R.G. Abaszade, A. Singh, S. Arya, G. Koushik, S.I. Yusifov and E.A. Khanmamadova2.1 Introduction 322.2 The Possibilities of Using Nanomaterials in Nanoelements 332.3 Nano-MOSFETs 342.4 Double-Gate MOSFET 372.5 FinFET 392.6 TFET 452.7 Conclusion 523 Nanowire, Nanotube FET, and CNTFET 67R.G. Abaszade, A. Singh, S. Arya, G. Koushik and E.A. Khanmamadova3.1 Introduction 683.2 Nanostructured Materials 693.3 Nanowire Applications in Electronic Devices 723.4 Nanotube FETs and Applications 763.5 CNTFETs and Applications 783.6 Conclusion 824 Dielectric Modulated Extended Source DG-TFET–Based Label-Free Biosensor: Design and Analysis 97Nidhish Tiwari, Bharat Choudhary and Rajesh Saha4.1 Introduction 984.2 Review on FET as Biosensor Application 994.3 Device Architecture and Simulation Deck 1024.4 Results and Discussion 1044.5 Conclusion 1125 Gate-All-Around Transistors Transforming Future-Generation VLSI Scaling 123P. Vimala, A. Sharon Geege, T.S. Arun Samuel and N. Mohan Kumar5.1 Introduction 1245.2 Industry Roadmap on GAA FETs 1255.3 Comparison of GAA FETs Over MOSFET 1285.4 Gate-All-Around Devices: Design Architecture 1305.5 Horizontal Gate-All-Around Field-Effect Transistors 1315.6 Vertical Gate-All-Around Field-Effect Transistors 1335.7 Comparison of Lateral and Vertical GAA-FETs 1375.8 Challenges and Opportunities of GAA FETs in VLSI 1425.9 Applications 1445.10 Conclusion and Research Scopes 1456 Optimization of Gate-All-Around Field-Effect Transistors Design Using Genetic Algorithm 159Chinmayee Dutta, Ananya Dastidar, Kanhu Charan Bhuyan and Dillip Kumar Sahoo6.1 Introduction 1606.2 Structural Description of Gate-All-Around Field-Effect Transistor 1626.3 Fabrication Description of GAAFET 1656.4 Operational Principle of GAAFET 1676.5 Genetic Algorithm-Based Optimization of GAAFET Parameters 1776.6 Application of Gate-All-Around (GAA) FET and Its Comparison to Other Devices 180viii Contents6.7 Summary 1847 Bandgap Reference Circuits for AI Hardware: Challenges, Design Trade-Offs, and Optimization Strategies 189Mayank Kumar Singh, Rajasekhar Nagulapalli, Devarshi Mrinal Das and Mahendra Sakare7.1 Introduction 1907.2 Fundamentals of Bandgap Reference Circuits 1947.3 Evolution and Technological Advancements 2007.4 Design Procedure of BGR Circuits 2007.5 Challenges and Open Issues 2077.6 Future Research Directions 2077.7 Conclusion 2098 High-Speed Continuous-Time Linear Equalizers for AI and Machine Learning Hardware: Design, Optimization, and Linearity Enhancement 223Puneet Singh, Rahul Walia, Rajasekhar Nagulapalli and Mahendra Sakare8.1 Introduction 2248.2 Channel Impairments and Consideration 226Contents ix8.3 Continuous-Time Linear Equalizers 2298.4 Linearity-Improved CTLE Architecture and Circuit Design 2348.5 Simulation Results and Discussion 2388.6 Conclusion 2439 Negative-Capacitance FET 253Katuri Yeshwanth, Sanket Saxena, Suman Lata Tripathi and Balwinder Raj9.1 Introduction 2549.2 Simulated Device Architecture 2629.3 Simulated Device Architecture 2649.4 Simulation Result 2679.5 Electrostatic Behavior in NC-FETs 2709.6 Comparison of NMOS and NC-FET 2769.7 Conclusion 27710 Adoption of Artificial Intelligence and Machine Learning Technique for Advanced Systems Design 289Vidhya S. G., Afsha Firdose, Siddartha B. K., Manu Y. M., Dhruva M. S. and Nishchitha T. S.10.1 Introduction 29010.2 The Impact of Artificial Intelligence (AI) and Machine Learning (ML) on System Design 29810.3 Basic Concepts in Artificial Intelligence (AI) and System Design 30510.4 Selected Popular Applications of Artificial Intelligence (AI) Based-Driven System 31011 A Comparative Study on Credit Card Fraud Ensnaring through Machine Learning 323Sangram Panigrahi, Sushree Bibhuprada B. Priyadarshini, Pritam Pradhan, Adyasha Upasana, Jyoti Ranjan Sahoo, B. S. Byomkesh and Sanjoy Mondal11.1 Introduction 32411.2 Literature Review 33111.3 Machine Learning Classifiers 333Contents xi11.4 Credit-Card Fraud Detection System (CFDS) 33711.5 Performance Evaluation 34011.6 Summary 34112 AI-Driven VLSI: Revolutionizing Semiconductor Design and Optimization 345M. Bharathi, G. Sandhyakumari, N. Ashok Kumar, N. Padmaja, Krithikaa Mohanarangam, V. Jalaja and Yasha Jyothi M. Shirur12.1 Introduction 34612.2 Role of AI in VLSI Design and Optimization 350\12.3 Artificial Intelligence (AI) Applications in Electronic Design Automation (EDA) with Case Study 35212.4 Enhancing Power, Performance, and Area (PPA) Using AI 35712.5 AI in FPGA and ASIC Design 35812.6 Artificial Intelligence (AI) Driven Automation in VLSI Physical Design 35912.7 Artificial Intelligence (AI) for Testing and Verification in VLSI 36212.8 Challenges in AI-Driven VLSI Design 36812.9 Future Directions 36912.10 Conclusion 37013 Performance Assessment of On-Chip Interconnects Using Neural Network Techniques 383N. Ashok Kumar, M. Bharathi, P. Nagarajan, Shaik Javid Basha, N. Geetha Rani and N. Praveen Kumar13.1 Introduction 38413.2 Network-on-Chip Architecture 39113.3 NN Operations on GPU 39913.4 Tile-Level Thermal Budget Constraint 40613.5 Heterogeneity of Applications and Tasks 40813.6 NoC Topology 40813.7 Scalability 41214 AI/ML-Based Approaches to VLSI Design Methodologies 427Jami Venkata Suman, Kolluru Anuhya, Kalisetti Purushotham Prasad, A. Swetha Priya, Omprakash Gurrapu and G.T. Chandra Sekhar14.1 Introduction 42814.2 Research Method 43214.3 Result and Discussion 43514.4 Conclusion 44714.5 Future Scope 44815 In-Memory Computing Using Memristors 459G. H. V. S. M. Soma Sai, P. Ramakrishna, Kala S. and Nalesh S.15.1 Introduction to Memristors 46015.2 Emergence of Memristors 46115.3 Memristor Device Physics and Fabrication 46215.4 Key Developments and Applications 46215.5 Existing Memristor Model 46215.6 Memristor-Based Logic Families 46415.7 Memristor-Based Computing Architectures 46415.8 IMPLY Logic 46515.9 IMPLY-Based Circuits 46715.10 Adders 47015.11 Design of Logic Gates 47615.12 Multiplier 47915.13 Reliability Challenges in IMPLY-Based In-Memory Computing with Memristors 48115.14 Challenges Hindering Memristor Commercialization 48215.15 Emerging Opportunities in Memristor Technology 48315.16 Summary 48316 Design of Voice of Care Using Personalized Voice Technology for Emotional Support and Digital Well-Being 489Selvakumar V. S., J. Saranya, Dinesh K. and Gayathri S. R.16.1 Introduction 49016.2 Literature Review 49216.3 System Architecture and Implementation 49416.4 Methodology 50616.5 Results 51116.6 Future Enhancements 51416.7 Conclusion 51517 Revolutionizing Agricultural Logistics Using Automated Sorting System 527Selvakumar V. S., Saranya J., Archana D. and Harini N.17.1 Introduction 52817.2 Literature Survey 52917.3 Proposed Solution and System Overview 53017.4 System Architecture 53317.5 Methodology 53917.6 Results and Discussion 54317.7 Conclusion 546Contents xvii17.8 Future Work 54918 VLSI Circuits and Systems for Smart Agriculture: Innovations and Applications 559M. Bharathi, Madhurima V., N. Ashok Kumar, Saleha Tabassum, G. Sandhyakumari, Yasha Jyothi M. Shirur and Krithikaa Mohanarangam18.1 Introduction 56018.2 Low-Power VLSI Circuits for Smart Agriculture 56418.3 Energy-Efficient Sensor Nodes 56518.4 Low-Power Microcontrollers and Processors 56618.5 Power Management Techniques for Remote Agriculture Applications 56718.6 VLSI-Based Sensor Systems for Agriculture Applications 56718.7 Wireless Communication and IoT Integration in Agriculture 57018.8 VLSI Solutions Based on Cloud Computing and Edge Computing 57118.9 Precision Agriculture using VLSI Circuits 57318.10 VLSI Circuits for Greenhouse and Controlled Environment Agriculture 57518.11 Conclusion 57719 Fundamentals of Memristors: Principles, Characteristics, and Emerging Applications 589Rajib Sutradhar, Himangshu Jyoti Gogoi, Kuldeep Gogoi, Bijoy Barman, P. Puspa Devi and Dipjyoti Das19.1 Introduction 59019.2 Materials for Memristors 60019.3 Memristor Characterization 60519.4 Multiscale Simulation of Resistive Switching: Bridging Materials, Devices, and Systems 61619.5 Future Scope and Challenges 62019.6 Conclusion 62120 ZnO Nanotube–Based Biosensors for the Detection of DNA Bases: A Comparative Study on Cytosine and Thymine Sensing 635Indranil Maity and Siddhartha Bhattacharya20.1 Introduction 63620.2 Computational Methodology 63820.3 Results and Discussion 63920.4 Comparative Analysis on Type-1 and Type-2 Systems 65420.5 Application of Artificial Intelligence (AI) and Machine Learning (ML) in Bio-Sensors 65520.6 Conclusion 65721 Impact of Artificial Intelligence and Machine Learning on the Design of Systems and the Future 663Neha Tyagi21.1 Introduction 66421.2 Defining Applications of Artificial Intelligence and Machine Learning 66521.3 The Necessity of Artificial Intelligence (AI) and Machine Learning (ML) in Modern System Design 66621.4 Objectives 66821.5 Historical Context and Evolution 67121.6 Traditional System Design Approaches 67521.7 The Transformation Brought by Artificial Intelligence (AI) and Machine Learning (ML) 67621.8 Case Study for AI-Driven System Evolution in E-Commerce 67821.9 The Role of Data in Artificial Intelligence (AI) Driven System Design 67921.10 Challenges in AI-Driven System Design 68221.11 Future Outlook 68421.12 Conclusion 68622 Non-Volatile Memory Devices for Neuromorphic Computing 695Debasis Das22.1 Introduction 69622.2 Understanding the Neuro-Synaptic Behavior 69922.3 Implementing Neuromorphic Hardware Using eNVM Devices 70322.4 Resistive Memory Device 70822.5 Spintronics Devices 71222.6 Conclusion 720References 72023 Cross-Voter Detection Using Zynq 7000 SoC Kit 727Arun Kumar Manoharan, Nagarjuna Telagam, Nehru Kandasamy, Menakadevi Nanjundan and Balwinder Raj23.1 Introduction 72823.2 Cross-Voting Detection Algorithms 73023.3 FPGA-Based Cross-Voting Detection 73123.4 Zynq 7000 FPGA Applications 73423.5 Edge Computing in Elections 73523.6 System Model 73823.7 Algorithm 74223.8 Working Mechanism 742xxii Contents23.9 Working on the Zynq Board 74323.10 Results and Discussions 74523.11 Conclusion 747Bibliography 748Index 761