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FeFET Devices, Trends, Technology and Applications
| FeFET Devices, Trends, Technology and Applications |
| Autore | Raj Balwinder |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (353 pages) |
| Disciplina | 621.3815/284 |
| Altri autori (Persone) |
RahiShiromani Balmukund
YadavNandakishor |
| Collana | Semiconductor devices and their applications |
| Soggetto topico |
Field-effect transistors
Ferroelectric devices |
| ISBN |
9781394287307
1394287305 9781394287284 1394287283 9781394287291 1394287291 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911020036703321 |
Raj Balwinder
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Integrated Devices for Artificial Intelligence and VLSI : VLSI Design, Simulation and Applications
| Integrated Devices for Artificial Intelligence and VLSI : VLSI Design, Simulation and Applications |
| Autore | Raj Balwinder |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (382 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
TripathiSuman Lata
ChaudharyTarun RaoK. Srinivasa SinghMandeep |
| Soggetto topico |
Artificial intelligence - Data processing
Integrated circuits - Very large scale integration |
| ISBN |
1-394-20515-5
1-394-20514-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNINA-9911020437803321 |
|
Raj Balwinder
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||