Integrated Devices for Artificial Intelligence and VLSI : VLSI Design, Simulation and Applications

Integrated Devices for Artificial Intelligence and VLSI VLSI Design, Simulation and Applications

With its in-depth exploration of the close connection between microelectronics, AI, and VLSI technology, this book offers valuable insights into the cutting-edge techniques and tools used in VLSI design automation, making it an essential resource for anyone seeking to stay ahead in the rapidly evolv...

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Detalles Bibliográficos
Autor principal: Raj, Balwinder (-)
Otros Autores: Tripathi, Suman Lata, Chaudhary, Tarun, Rao, K. Srinivasa, Singh, Mandeep
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Materias:
Artificial intelligence > Data processing.
Integrated circuits > Very large scale integration.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009843332006719
Tabla de Contenidos:
  • Cover
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Chapter 1 Comparative Analysis of MOSFET and FinFET
  • 1.1 Introduction
  • 1.1.1 Scaling Issue
  • 1.1.2 Problems in MOSFET
  • 1.2 Double Gate
  • 1.3 Advantages and Disadvantage of MOSFET
  • 1.4 MOSFET Drawbacks
  • 1.5 FinFET
  • 1.6 SOI-FinFET
  • 1.7 Issues with FinFET-Based Technology
  • 1.8 Advantage of FinFET
  • 1.9 Drawbacks of FinFET
  • 1.10 Applications of FinFET Technology
  • 1.11 Conclusion
  • References
  • Chapter 2 Nanosheet FET for Future Technology Scaling
  • 2.1 Introduction
  • 2.2 Device Description and Simulation Parameters
  • 2.2.1 Analysis of the Results Obtained
  • 2.2.2 Impact of Variation in Width Across Various Thickness Values on Device Characteristics
  • 2.2.3 Transfer Characteristics
  • 2.2.4 Impact of Geometrical Variations on ON Current
  • 2.2.5 Impact of Geometrical Variations on OFF-Current
  • 2.2.6 Impact of Geometrical Variations on Switching Ratio
  • 2.2.7 Impact of Geometrical Variations on Threshold Voltage
  • 2.2.8 Impact of Geometrical Variations on Subthreshold Swing
  • 2.2.9 Impact of Geometrical Variations on DIBL
  • 2.2.10 Comparison with Previous Works
  • 2.3 Conclusions
  • References
  • Chapter 3 Comparison of Different TFETs: An Overview
  • 3.1 Introduction
  • 3.2 Tunnel FET
  • 3.3 Gate Engineering
  • 3.3.1 Oxide-Thickness and Dielectric-Constant of Gateoxide
  • 3.3.2 Multiple Gates
  • 3.3.3 Spacer Engineering
  • 3.4 Tunneling-Junction Engineering
  • 3.4.1 Doping of Source
  • 3.4.2 Heterojunctions
  • 3.5 Materials Engineering
  • 3.5.1 Germanium
  • 3.5.2 III-V Semiconductors
  • 3.5.3 Nanowires
  • 3.6 Conclusion
  • References
  • Chapter 4 GaAs Nanowire Field Effect Transistor
  • 4.1 Introduction
  • 4.1.1 Semiconductor Nanowires
  • 4.1.2 Metal Nanowires
  • 4.1.3 Oxide Nanowires
  • 4.1.4 Hybrid Nanowires.
  • 4.1.5 Biological Nanowires
  • 4.2 Properties of Nanowires
  • 4.2.1 Electrical Properties of Nanowire
  • 4.2.2 Mechanical Properties
  • 4.2.3 Optical Properties of Nanowire
  • 4.2.4 Nonlinear Optical Properties
  • 4.2.5 Photovoltaic Properties
  • 4.3 Nanowire-FET
  • 4.4 Proposed Work (GaAs Nanowire-FET)
  • 4.5 Conclusion
  • References
  • Chapter 5 Graphene Nanoribbon for Future VLSI Applications: A Review
  • 5.1 Introduction
  • 5.1.1 Significance of Nano-Scale Reign
  • 5.1.2 Importance of Repeaters
  • 5.1.3 Interconnect Models
  • 5.1.4 Lumped Model
  • 5.1.5 Distributed Model
  • 5.1.6 Aluminum and Copper as Interconnects
  • 5.1.7 Graphene Nanoribbon as Interconnects
  • 5.1.8 Classification of GNRs
  • 5.1.9 Fundamental Physics
  • 5.1.10 According to Structure and Conductivity
  • 5.1.11 GNR Field Effect Transistor (GNRFET)
  • 5.1.12 Model Development of GNRFET
  • 5.1.13 Pros and Cons of GNRFET
  • 5.2 Future Applications of Graphene and Graphene-Based FETs
  • References
  • Chapter 6 Ferroelectric Random Access Memory (FeRAM)
  • 6.1 Introduction
  • 6.1.1 Basic Characteristics of Ferroelectric Capacitors
  • 6.1.2 FRAM Fabrication Process
  • 6.2 Structure of Ferroelectric Memory Cells in Capacitor-Type FRAM Devices
  • 6.2.1 A Capacitor-Type FRAM with a Memory Cell Resembling DRAM
  • 6.3 Write/Read Operations in the FRAM Using a Capacitor- Type Memory Cell that Resembles a DRAM
  • 6.4 Other Capacitor-Type FRAM
  • 6.5 FRAM of FET Type
  • 6.6 Memory Utilizing a Ferroelectric Tunnel Junction
  • 6.6.1 Previous Ferroelectric Memory Designs
  • 6.7 Cross Point Matrix Array
  • 6.8 Ferroelectric Shadow RAMs
  • 6.9 2T2C Ferroelectric RAM Architecture
  • 6.9.1 Evaluation of FRAM Devices' Reliability
  • 6.9.2 Comparative Analysis of FeRAM to Other Memory Technologies
  • 6.10 FeRAM vs. EEPROM
  • 6.11 FeRAM vs. Static RAM
  • 6.12 FeRAM vs. Dynamic RAM.
  • 6.13 FeRAM vs. Flash Memory
  • 6.13.1 Uses of FRAM Devices
  • 6.14 Conclusion and Upcoming Trends
  • References
  • Chapter 7 Applications of AI/ML Algorithms in VLSI Design and Technology
  • 7.1 Introduction
  • 7.2 Artificial Intelligence and Machine Learning
  • 7.3 AI/ML Algorithms
  • 7.4 Supervised Machine Learning (SML)
  • 7.5 Classification Techniques
  • 7.6 K-Nearest Neighbors (KNN)
  • 7.7 Support Vector Machine (SVM)
  • 7.8 Linearly Separable Classification
  • 7.9 Decision Tree Classifier (DTC)
  • 7.10 Performance Measures in Classification
  • 7.11 Unsupervised Machine Learning (UML)
  • 7.12 Hierarchical Clustering
  • 7.13 Partitional Clustering
  • 7.14 K-Means
  • 7.15 Fuzzy (soft) Clustering
  • 7.16 Cluster Validation Measures
  • 7.17 Internal Clustering Validation Measures
  • 7.18 External Clustering Validation Criteria
  • 7.19 Limitation and Challenges - VLSI
  • References
  • Chapter 8 Advancement of Neuromorphic Computing Systems with Memristors
  • 8.1 Introduction
  • 8.1.1 Evolution in Neural Networks
  • 8.1.2 Study Plan and Difficulties in Exhibiting Effective Neuromorphic Computing Systems
  • 8.1.3 Hardware for Neuromorphic Systems
  • 8.1.4 Device-Level Perspective
  • 8.1.5 Electrical Circuits to Realize Neurons
  • 8.1.6 Broad Applications of Neuromorphic Computing
  • 8.2 Summary
  • References
  • Chapter 9 Neuromorphic Computing and Its Application
  • 9.1 Introduction
  • 9.2 Evolution of Neuroinspired Computing Chips
  • 9.3 Science Behind Brain Physics
  • 9.4 Limitations of Semiconductor Devices
  • 9.5 Various Combination of Networks
  • 9.5.1 ANN-SNN Hybrid
  • 9.5.2 Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN) Hybrid
  • 9.5.3 Deep Reinforcement Learning (DRL) Hybrid
  • 9.5.4 Ensemble Hybrid
  • 9.5.5 Different Types of Neural Networks
  • 9.6 Artificial Intelligence.
  • 9.7 A Summary of Neuromorphic Hardware Methodologies
  • 9.8 Neuromorphic Computing in Robotics
  • 9.8.1 Sensor Processing and Perception
  • 9.8.2 Motor Control and Movement
  • 9.8.3 Neuromorphic Hardware Advances
  • 9.8.4 Brain-Inspired Learning Algorithms
  • 9.9 Challenges in Neuromorphic Computing
  • 9.9.1 Language Understanding and Interpretation
  • 9.9.2 Sentiment Analysis and Emotion Recognition
  • 9.9.3 Natural Language Generation
  • 9.9.4 Language Translation and Multilingual Processing
  • 9.9.5 Dialogue Systems and Conversational Agents
  • 9.9.6 Language Modeling and Prediction
  • 9.9.7 Text Summarization and Information Extraction
  • 9.10 Applications of Neuromorphic Computing
  • 9.10.1 Medicines
  • 9.10.2 Artificial Intelligence [AI]
  • 9.10.3 Imaging
  • 9.10.4 Sensor Processing and Perception
  • 9.10.5 Motor Control and Movement
  • 9.10.6 Autonomous Navigation and Mapping
  • 9.10.7 Human-Robot Interaction and Collaboration
  • 9.10.8 Adaptive and Learning Capabilities
  • 9.10.9 Task Planning and Decision Making
  • 9.10.10 Robustness and Fault Tolerance
  • 9.10.11 Some More Applications
  • 9.11 Conclusion
  • References
  • Chapter 10 Performance Evaluation of Prototype Microstrip Patch Antenna Fabrication Using Microwave Dielectric Ceramic Nanocomposite Materials for X-Band Applications
  • 10.1 Introduction
  • 10.2 Materials and Methods
  • 10.3 Results and Discussion
  • 10.4 Conclusions
  • References
  • Chapter 11 Build and Deploy a Smart Speaker with Biometric Authentication and Advanced Voice Interaction Capabilities
  • 11.1 Introduction
  • 11.2 Cybersecurity Risk as Smart Speakers Don't Have an Authentication Process
  • 11.3 Related Work
  • 11.4 Overview of Biometric Authentication and the Voice Algorithm-Based Smart Speaker
  • 11.5 Conclusion and Discussion
  • Acknowledgements
  • References.
  • Chapter 12 Boron-Based Nanomaterials for Intelligent Drug Delivery Using Computer-Aided Tools
  • 12.1 Introduction
  • 12.2 Computational Details
  • 12.3 Results and Discussion
  • 12.3.1 Interaction of Anisamide with 7-Membered Ring of B40
  • 12.3.2 Interaction of Anisamide with 6-Membered Ring of B40
  • 12.3.3 Interaction of 5F-Uracil with the Heptagonal Ring of B40+7AN Complex (AN on Heptagonal Ring)
  • 4012.3.4 Interaction of 5F-Uracil with the Hexagonal Ring of B40+7AN Complex (AN on Heptagonal Ring)
  • 12.3.5 Interaction of 5F-Uracil with the Heptagonal Ring of B40+6AN Complex (AN on Hexagonal Ring)
  • 12.3.6 Interaction of 5F-Uracil with the Hexagonal Ring of B40+6AN Complex (AN on Hexagonal Ring)
  • 12.3.7 Stability in Aqueous Solution
  • 12.3.8 Drug Release
  • Acknowledgement
  • Conflict of Interest
  • References
  • Chapter 13 Design and Analysis of Rectangular Wave Guide Using an HFSS Simulator
  • 13.1 Background
  • 13.2 Introduction
  • 13.3 Mathematical Computations
  • 13.4 Numerical Analysis
  • 13.5 Conclusion
  • References
  • Index
  • Also of Interest
  • EULA.

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