Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning

Udit Mamodiya, Suman Lata Tripathi, Deepika Ghai, Deepak Kumar Jain

• Publisher: Wiley-Scrivener
• Year: 2026
• Print ISBN: 9781394355280
• eBook ISBN: 9781394355297
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Engineering General
• Index: Technology & Engineering

Gain a competitive edge in the semiconductor industry with this essential guide, which provides the practical insights and machine learning techniques needed to optimize the fabrication of hybrid nanodevices for integrated circuits.

Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning explores the intersection of advanced manufacturing techniques and machine learning applications in the field of nanotechnology, specifically focusing on hybrid nanodevices for integrated circuits. This book provides a comprehensive understanding of how machine learning algorithms and techniques can optimize the fabrication processes of hybrid nanodevices, improving their efficiency, reliability, and performance in integrated circuit applications. The book begins with an introduction to the fundamentals of hybrid nanodevice fabrication and the role of machine learning in enhancing these processes. It then delves into various machine learning algorithms and models used for process optimization, quality control, and predictive maintenance in integrated circuit fabrication. Case studies and practical examples illustrate real-world applications of machine learning in improving yield, reducing costs, and accelerating time-to-market for hybrid nanodevices. It also addresses the pressing need for a comprehensive guide on machine learning applications in nanodevice fabrication. It provides researchers, engineers, and industry professionals with practical insights for implementing machine learning techniques to tackle challenges such as variability reduction, defect detection, and process optimization. By bridging the gap between theory and practice, the book equips readers with the knowledge and tools necessary to leverage machine learning for a competitive advantage in the semiconductor industry.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Preface
• 1 Challenges and Limitations in Implementation: Nanodevice Fabrication Efficiency Using Machine Learning
• 2 A Comprehensive Review of Machine Learning Algorithms and their Utilization in Nanodevice Fabrication
• 3 Integrating Deep Learning in Rolling Process Design for Nanocomposites: A Novel Approach to Strength Prediction
• 4 Future Directions in Machine Learning–Driven Nanodevice Fabrication
• 5 Unlocking Machine Learning: Revolutionizing Fabrication of Nanocircuitry
• 6 Enabling Smarter Nanosystems: The Role of AI and Supervised Machine Learning in Nanotechnology
• 7 Harnessing Unsupervised Machine Learning for Advanced Nanodevice Fabrication
• 8 Supervised Learning Models for Fabrication Optimization in Semiconductor Nanodevices
• 9 Advancements and Challenges in Nanomaterial Integration for Next-Generation Devices
• 10 An Efficient Exploration of Process Optimization through Deep Learning Approaches
• 11 Machine Learning Approach for Quantum Dots Synthesis
• 12 Deep Learning for Process Optimization: Techniques, Applications, and Future Directions
• 13 Advanced ML Algorithms for Nanotechnology
• 14 Integrating Machine Learning and Nanotechnology: Driving Innovation and Sustainable Solutions
• 15 Case Studies in ML-Driven AI Nanodevice Fabrication
• 16 Data Acquisition and Preprocessing Techniques for Effective Machine Learning
• 17 Fundamentals of Machine Learning for Nanotechnology
• 18 Optimizing Hybrid Nanodevice Fabrication Efficiency through Unsupervised Machine Learning Approaches
• 19 Emerging Trends in Micro and Nano Manufacturing: A Survey of Modern Technologies and Future Prospects
• 20 Exploring Machine Learning in Nanotechnology
• 21 Machine Learning as a Tool in Nanodevice Fabrication
• 22 Optimizing Hybrid Nanodevice Fabrication Efficiency through Machine Learning: Applications in Precision Control and Defect Reduction
• Index
• Also of Interest
• End User License Agreement