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Introduction

Neuromorphic computing has gained significant attention in recent years due to its potential to revolutionize traditional computing architectures by mimicking the structure and function of the human brain. Spin-transfer torque oscillators (STOs) have emerged as a promising candidate for neuromorphic computing due to their low power consumption, high-speed operation, and scalability. This thesis focuses on the utilization of STOs for neuromorphic computing applications, exploring their potential to significantly improve the efficiency and performance of neural networks.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Neuromorphic Computing
2.2 Introduction to Spin-transfer Torque Oscillators
2.3 Previous Research on STOs for Neuromorphic Computing
2.4 Comparison of STOs with Other Neuromorphic Devices
2.5 Advantages and Limitations of Using STOs for Neuromorphic Computing
2.6 STO-based Neurosynaptic Systems
2.7 STO-based Neural Network Architectures
2.8 Challenges and Opportunities for STOs in Neuromorphic Computing
2.9 Future Directions in STO-based Neuromorphic Computing
2.10 Conclusion

Chapter 3: System Design and Methodology
3.1 Overview of System Design
3.2 Selection of STO Devices
3.3 Integration of STOs into Neuromorphic Architectures
3.4 Implementation of STO-based Neural Networks
3.5 Data Encoding and Processing Techniques
3.6 Hardware and Software Requirements
3.7 Simulation and Testing Methodologies
3.8 Performance Evaluation Metrics

Chapter 4: System Implementation
4.1 STO Fabrication and Characterization
4.2 Development of STO-based Neuromorphic Computing Platform
4.3 Integration of STOs with Neuromorphic Circuits
4.4 Programming and Configuration of STO-based Neural Networks
4.5 Optimization of STO Performance Parameters
4.6 Testing and Validation of STO-based Neuromorphic Systems
4.7 Analysis of Results
4.8 Comparison with Existing Neuromorphic Solutions

Chapter 5: Conclusion and Summary
5.1 Conclusion
5.2 Summary of Findings
5.3 Contributions of the Study
5.4 Implications for Future Research
5.5 Recommendations for Practitioners
5.6 Conclusion

Thesis Overview: Spin-transfer torque oscillators (STOs) have emerged as a promising technology for neuromorphic computing applications. This thesis explores the utilization of STOs in neuromorphic architectures, focusing on their potential to improve the efficiency and performance of neural networks. The introductory chapter provides a background of the study, problem statement, objectives, scope, limitations, significance, and structure of the thesis. The literature review chapter discusses the current state of neuromorphic computing, STO technology, previous research, advantages, limitations, challenges, and future directions. The system design and methodology chapter outlines the design, selection, integration, implementation, and testing methodologies for STO-based neuromorphic systems. The system implementation chapter details the fabrication, development, optimization, testing, and analysis of STO-based neuromorphic platforms. The conclusion and summary chapter presents the findings, contributions, implications, recommendations, and future research directions. Through this thesis, the potential of STOs for neuromorphic computing is explored, paving the way for advancements in cognitive computing technologies.

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