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Enhancing Hybrid Nanodevice Fabrication Efficiency using Machine Learning

Edited by Udit Mamodiya, Suman Lata Tripathi, Deepika Ghai, and Deepak Kumar Jain
Copyright: 2026 | Expected Pub Date:2026//
ISBN: 9781394355280 | Hardcover |
492 pages
Price: $225 USD

One Line Description
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.

Audience
Professionals, academics, and researchers working in semiconductor manufacturing, nanotechnology, and integrated circuit design.

Description
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.

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Author / Editor Details
Udit Mamodiya, PhD is an Associate Professor and Associate Dean of Research at Poornima University with more than 12 years of experience. He has authored ten books and more than 50 papers, published more than 50 utility patents, and holds 20 design patents and copyrights. His research interests include renewable energy sources, reliability analysis, expert systems, and decision support systems.

Suman Lata Tripathi, PhD is a Professor at Lovely Professional University with more than 22 years of experience in academics and research. She has authored and edited more than 30 books and published more than 140 research papers in international journals, conference proceedings, and e-books, 14 Indian patents, and four copyrights. Her areas of expertise include microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design testing, and advanced FET design for IoT and embedded system design.

Deepika Ghai, PhD is an Assistant Professor at Lovely Professional University with more than five years of experience in academics. She has published two books and more than 35 research papers in refereed journals and conferences. Her areas of expertise include signal and image processing, biomedical signal and image processing, AI and machine learning, and VLSI signal processing.

Deepak Kumar Jain, PhD is an Associate Professor and Senior Scientist in the School of Artificial Intelligence at Dalian University of Technology. He has presented several papers in peer-reviewed conferences and authored and coauthored numerous studies in internationally reputed journals. His research interests include deep learning, machine learning, pattern recognition, and computer vision.

Table of Contents
Preface
1. Challenges and Limitations in Implementation: Nanodevice Fabrication Efficiency Using Machine Learning
Amit Kumar Jain, Tarun Mishra and Mohamed M. Awad
1.1 Introduction
1.1.1 Overview of Hybrid Nanodevice Fabrication
1.1.2 Role of Machine Learning in Nanodevice Fabrication
1.1.3 Scope of the Chapter
1.2 Related Study
1.3 Case Studies for ML-Driven Nanodevice Fabrication
1.3.1 Defect Detection and Quality Control Using Computer Vision
1.3.2 Process Optimization in Nanoparticle Synthesis
1.3.3 Self-Optimizing Photovoltaic Nanostructures
1.3.4 Autonomous Nanofabrication for Biomedical Devices
1.3.5 Enhancing Battery Life through Nanodevice-Driven ML Algorithms
1.4 Comparative Study between Challenges and Limitations in Hybrid Nanodevice Fabrication Efficiency Using ML
1.4.1 Nature of the Issue
1.4.2 Data-Related Issues
1.4.3 Model Complexity and Interpretability
1.4.4 Real-Time Adaptation and Scalability
1.4.5 Integration with Existing Systems
1.4.6 Cost of Implementation
1.5 Applications
1.5.1 Biomedicine and Healthcare
1.5.2 Energy Harvesting and Storage
1.5.3 Environmental Sensing and Remediation
1.5.4 Advanced Electronics and Computing
1.5.5 Optoelectronics and Photonics
1.5.6 Quantum Computing and Information Processing
1.6 Advantages of ML in Hybrid Nanodevice Fabrication Efficiency
1.6.1 Enhanced Precision and Accuracy
1.6.2 Real-Time Process Optimization
1.6.3 Reduction in Manufacturing Costs
1.6.4 Faster Time to Market
1.6.5 Scalability and Customization
1.7 Disadvantages of ML in Hybrid Nanodevice Fabrication Efficiency
1.7.1 Data Quality and Availability
1.7.2 Model Interpretability
1.7.3 High Computational Costs
1.7.4 Scalability Issues
1.7.5 Dependence on Expertise
1.8 Future Scope
1.8.1 Advanced Hybrid Fabrication Techniques
1.8.2 Integration with Quantum Computing
1.8.3 Cross-Disciplinary Synergy
1.8.4 Self-Learning Systems
1.8.5 Personalized Nanodevices
1.9 Conclusion
Bibliography
2. A Comprehensive Review of Machine Learning Algorithms and their Utilization in Nanodevice Fabrication
Basudha Dewan
2.1 Introduction
2.2 Universal ML Model
2.3 Types of ML Algorithms
2.4 Challenges in ML
2.5 Recent Developments in ML
2.6 Ethical Concerns and Fairness in ML
2.7 Role of ML in Nanodevice Fabrication
2.8 Proposed Model
2.9 Conclusion
References
3. Integrating Deep Learning in Rolling Process Design for Nanocomposites: A Novel Approach to Strength Prediction
Amit Tiwari, Payal Bansal, Rachid Amrousse and Seitkhan Azat
3.1 Introduction
3.2 Database Collection
3.3 Computational Modeling
3.4 Results and Discussion
3.4.1 Determining Hidden Layers and Training of ANN
3.4.2 Mean Square Error Plot of Hardness Value and Tensile Strength Value
3.4.3 Error Histogram of Hardness Value and Tensile Strength Value
3.4.4 Function Fit Plot of Hardness Value and Tensile Strength Value
3.4.5 Regression Analysis Plot of Hardness Value and Tensile Strength Value
Conclusion
References
4. Future Directions in Machine Learning–Driven Nanodevice Fabrication
Wasswa Shafik
4.1 Introduction
4.2 Fundamentals of Nanodevice Fabrication
4.2.1 Traditional Methods
4.2.2 Emergence of ML
4.3 ML Techniques in Nanodevice Fabrication
4.3.1 Supervised Learning
4.3.2 Unsupervised Learning
4.3.3 Reinforcement Learning
4.4 Applications of ML in Nanodevice Fabrication
4.4.1 Optimizing Device Performance
4.4.2 Predictive Maintenance
4.4.3 Automated Design Generation
4.5 Challenges and Limitations
4.5.1 Data Quality and Quantity
4.5.2 Interpretability and Explainability
4.5.3 Ethical Considerations
4.6 Future Research Directions
4.6.1 Hybrid Approaches
4.6.2 More Data Than Model Scenarios
4.6.3 Ethical and Practical Challenges
4.6.4 Hybrid Approaches Combining ML andbPhysics-Based Models
4.6.5 Transfer Learning for Limited Data Scenarios
4.6.6 Real-Time Adaptive Control Systems
4.6.7 Quantum Dot Synthesis
4.7 Conclusion
References
5. Unlocking Machine Learning: Revolutionizing Fabrication of Nanocircuitry
Mohammed Firdos Alam Sheikh, Nikhil Kumar Goyal, Udit Mamodiya and Tien Anh Tran
Introduction
Nanofabrication Techniques
Chemical Methods
Physical Methods
Challenges in Traditional Nanofabrication Techniques
Key Concepts in ML of Fabrication Processes
Revolutionizing Lithography with ML
Computational Lithography and Resolution Enhancement
Improvements in Mask Designing and Optimization
Adding ML to Etch Processes: Etch Proximity Correction
Introduction to New Materials and Scientific Advance
Advantages of ML
Time-Based Optimizations for Fabrication Scheduling and Resource Allocation Using ML
Interdiscipline Nanocircuit Fabrication Methods Overcoming Challenges
Conclusion: The Transformative Potential of ML in Nanofabrication
References
6. Enabling Smarter Nanosystems: The Role of AI and Supervised Machine Learning in Nanotechnology
Indra Kishor, Udit Mamodiya, Sayed Sayeed Ahmad, Priya Goyal and Deepti Dwivedi
6.1 Introduction
6.2 Literature Review
6.3 Methodology
6.3.1 Proposed Work
6.3.2 Workflow
6.3.3 Workflow Diagram
6.3.4 Data Source, Tools, and Technologies
6.3.5 Step-by-Step Methodology
6.3.6 Supervised Learning Algorithm
6.4 Results
6.4.1 Key Findings
6.4.2 Statistical Analysis
6.4.3 Alignment with Objectives and Methodology
6.5 Discussion
6.6 Conclusion
References
7. Harnessing Unsupervised Machine Learning for Advanced Nanodevice Fabrication
Indra Kishor, Udit Mamodiya, Sayed Sayeed Ahmad, Priya Goyal and Deepti Dwivedi
7.1 Introduction
7.2 Literature Review
7.3 Methodology
7.3.1 Data Collection
7.3.2 Data Preprocessing
7.3.3 Unsupervised Learning Techniques
7.3.4 Anomaly Detection
7.3.5 Evaluation Metrics
7.3.6 Experimental Workflow
7.3.7 Integration of Manufacturing Workflows
7.3.8 Statistical Validation
7.4 Results
7.4.1 Clustering Results
7.4.2 Dimensionality Reduction
7.4.3 Anomaly Detection
7.4.4 Integration and Scalability
7.5 Discussion
7.6 Conclusion
References
8. Supervised Learning Models for Fabrication Optimization in Semiconductor Nanodevices
Irfan Ahmad Pindoo and Suman Lata Tripathi
8.1 Introduction
8.1.1 Overview of Semiconductor Nanodevices
8.1.2 Fabrication Optimization Challenges
8.2 The Semiconductor Industry and Machine Learning
8.3 Semiconductor Fabrication Process
8.3.1 Key Stages in Semiconductor Manufacturing
8.3.2 Common Challenges and Variabilities
8.4 Applications of Supervised Learning in Fabrication Optimization
8.5 Machine Learning–Based Semiconductor Process Optimization
References
9. Advancements and Challenges in Nanomaterial Integration for Next-Generation Devices
Mukesh Chand, Pooja Rani, Charul Bapna and Garima Kachhara
9.1 Introduction
9.1.1 Overview of Nanomaterials
9.1.2 Definition and Properties of Nanomaterials
9.1.3 Classification of Nanomaterials
9.1.4 Historical Development and Milestones
9.1.5 Advantages of Nanomaterials
9.1.5.1 Superior Performance
9.1.5.2 Multiple Functions
9.1.5.3 Scalability with Respect to Miniaturized Systems
9.1.6 Challenges and Limitations
9.1.6.1 Scalability
9.1.6.2 Material Stability
9.1.6.3 Environmental and Health Concerns
9.2 Nanomaterials in Device Integration
9.2.1 Graphene in Electronics and Sensors
9.2.2 CNTs in Energy Storage
9.2.3 QDs in Optoelectronics and Solar Cells
9.2.4 TMDs in Photodetectors and Transistors
9.2.5 Nanomaterial Composites for Multifunctional Devices
9.3 Related Work
9.4 Fabrication Techniques for Nanomaterial Integration
9.4.1 Chemical Vapor Deposition
9.4.2 Mechanical Exfoliation
9.4.3 Solvothermal and Colloidal Synthesis
9.4.4 Self-Assembly Techniques
9.4.5 Additive Manufacturing and 3D Printing
9.4.6 Spray Deposition and Dip Coating
9.4.7 Lithographic Techniques
9.5 Challenges in Nanomaterial Integration
9.6 Conclusions and Future Directions
References
10. An Efficient Exploration of Process Optimization through Deep Learning Approaches
Nikhil Kumar Goyal, Monika Dandotiya, Monika Kumari, Shikha Sharma and A. Anushya
10.1 Introduction
10.1.1 What is Optimization?
10.1.2 Theoretical Perspective on Deep Learning for Optimization
10.1.2.1 Mathematics of Deep Learning
10.1.3 Methods for Optimization in Deep Learning
10.1.4 First-Order Optimization Methods
10.1.4.1 Stochastic Gradient Descent
10.1.4.2 AdaGrad
10.1.5 RMSProp
10.1.6 Adam
10.1.7 Second-Order Optimization Methods
10.1.7.1 Newton’s Method
10.1.7.2 Conjugate Gradient Method
10.2 Deep Learning Architectures for Process Optimization
10.2.1 Convolutional Neural Networks
10.2.2 Recurrent Neural Networks
10.2.3 Long Short-Term Memory Networks
10.2.4 Generative Adversarial Networks
10.2.5 Autoencoders
10.3 Challenges and Limitations in the Deep Learning Process Optimization Process
10.4 Conclusion
References
11. Machine Learning Approach for Quantum Dots Synthesis
Rajat Kumar Goyal, Nidhi Bharadwaj and Pramod Garhwal
11.1 Introduction
11.2 Basic and Operating Principles of ML
11.3 Various ML Algorithms for QD Research
11.3.1 Linear Regression
11.3.2 Artificial Neural Networks
11.3.3 Gradient Boosting
11.3.4 Random Forest
11.3.5 Other ML Algorithms
11.4 Summary and Future Perspectives
References
12. Deep Learning for Process Optimization: Techniques, Applications, and Future Directions
Randhir Singh Baghel, Bindiya Jain, Udit Mamodiya and Harkaran Singh
12.1 Introduction
12.2 Overview of Process Optimization
12.2.1 Problem Identification
12.2.2 Process Analysis
12.2.3 Modeling and Simulation
12.2.4 Optimization Techniques
12.2.5 Implementation
12.2.6 Monitoring and Continuous Improvement
12.3 Role of DL in Optimization
12.3.1 Handling Complex, High-Dimensional Data
12.3.2 Automated Feature Selection and Engineering
12.3.3 Optimization of Hyperparameters
12.3.4 Real-Time Decision-Making
12.3.5 Optimizing RL
12.3.6 Multiobjective Optimization
12.3.7 Continuous Learning and Adaptation
12.3.8 Scalability and Performance
12.4 Optimization in Industrial and Business Contexts
12.5 Applications of DL in Process Optimization
12.6 Deep Learning Applications in Supply Chain and Logistics Optimization
12.6.1 Demand Forecasting Using Deep Learning Models
12.6.2 Route Optimization Using Deep Learning Techniques
12.6.3 Inventory Management Using Deep Learning
12.6.4 Supply Chain Network Design Using Deep Learning
12.6.5 Demand–Supply Matching Using Deep Learning
12.6.6 Risk Management and Disruption Forecasting Using Deep Learning
12.6.7 Mult-Objective Deep Learning for Supply Chain Optimization
12.6.8 Future Trends and Challenges in Supply Chain and Logistics Optimization
12.6.9 Conceptual Diagram of Supply Chain Management
12.6.9.1 Design and Structure
12.6.9.2 Color Scheme and Layout
12.7 Challenges in Implementing DL for Process Optimization
12.7.1 Key Branches
12.7.2 Subnodes
Conclusion
References
13. Advanced ML Algorithms for Nanotechnology
R. Remya, Shaik Saniya, O. Jeba Singh and Umesh Sampath
13.1 Introduction
13.1.1 Analyzing Large Datasets
13.1.2 Designing and Discovering New Nanomaterials
13.1.3 More Efficient Hardware
13.2 Deep Learning for Nanoscale Imaging
13.2.1 Applications in Nanoscale Imaging
13.2.2 Procedures and Tools
13.2.3 Challenges
13.2.4 Emerging Trends
13.2.5 Tools and Frameworks
13.3 Graph Neural Networks for Molecular Structure
13.3.1 Molecule Graphical Representation
13.3.2 Message Passing Neural Network (MPNN)
13.3.3 Graph Convolutional Network
13.3.4 Graph Attention Network
13.3.5 Weisfeiler-Leman Graph Kernels
13.3.6 3D GNN
13.4 Quantum ML for Nanotechnology Applications
13.5 RL in Nanofabrication
13.5.1 Advantages of Using RL in Nanofabrication
13.6 Meta Learning for Metal Discovery
13.6.1 Why Metal Learning is Adapted for Metal Discovery?
13.6.2 Applications
13.7 Conclusion
References
14. Integrating Machine Learning and Nanotechnology: Driving Innovation and Sustainable Solutions
Shruti Gupta, Sourabh Kumar Jain and Gireesh Kumar
14.1 Introduction
14.2 Steps Involved in Building an ML Model
14.2.1 Preliminary Data Insights
14.2.2 Data Attributes Identification and Data Collection
14.2.3 Data Preparing and Refining Data
14.2.4 Feature Engineering
14.2.5 Development of ML Model
14.2.6 Hyperparameter Tuning
14.2.7 Validation of ML Model
14.3 How AI and Nanotechnology are Revolutionizing Healthcare and Safety
14.3.1 Delivering Medications with Pinpoint Accuracy
14.3.2 Tracking and Detecting Diseases
14.4 Ensuring Quality in Nanomanufacturing
14.5 Environmental Monitoring and Remediation
14.6 Advancements in Nanotechnology and Quantum Computing
14.7 AI and Nanotechnology: Challenges and Future Opportunities
14.8 Conclusion
References
15. Case Studies in ML-Driven AI Nanodevice Fabrication
Yogita Thareja, Sakshi Khullar and Parulpreet Singh
15.1 Introduction
15.2 Experimental Survey and Materials
15.3 Methodology
15.3.1 5W+1H Method
15.3.2 Testing Phases
15.3.3 Need of Testing
15.3.4 Healthcare Testing Domain
15.4 Results
15.5 Conclusion
Bibliography
16. Data Acquisition and Preprocessing Techniques for Effective Machine Learning
B. Sarada, C. Gazala Akhtar, N. Shaleen Saroj and Sanjeevini S. Harwalka
16.1 Introduction
16.1.1 Importance of Data Acquisition and Preprocessing in ML
16.1.2 Overview of Challenges and Objectives
16.2 Data Acquisition—Definition and Role in ML
16.2.1 Sources of Data
16.2.1.1 Primary Data Sources
16.2.1.2 Secondary Data Sources
16.2.2 Types of Data
16.2.2.1 Structured Data
16.2.2.2 Unstructured Data
16.2.2.3 Semistructured Data
16.2.3 Data Collection Tools and Techniques
16.2.3.1 APIs for Data Collection
16.2.3.2 Web Scraping with Python Tools
16.2.3.3 Data Collection through Surveys and Online Platforms
16.2.3.4 IoT Devices and Sensor Data Acquisition
16.3 Data Cleaning
16.3.1 Importance of Data Cleaning in Preprocessing
16.3.2 Preprocessing for Numerical Data: Handling Missing Data
16.3.2.1 Deletion Methods
16.3.2.2 Methods of Imputation
16.3.3 Detection and Removal of Outlier
16.3.3.1 The Z-Score Approach
16.3.3.2 The Technique of Interquartile Range
16.3.4 Reduction of Noise
16.3.4.1 Filtering Techniques
16.4 Data Transformation
16.4.1 Scaling Transformations: Normalization and Standardization
16.4.1.1 Min–Max Scaling
16.4.1.2 Z-Score Standardization
16.4.1.3 Robust Scaling
16.4.2 Feature Engineering
16.4.2.1 Creating Interaction Terms and Polynomial Features
16.4.2.2 Log Transformations and Binning
16.4.3 Encoding Categorical Data
16.4.3.1 One-Hot Encoding
16.4.3.2 Label Encoding
16.4.3.3 Ordinal Encoding for Categorical Variables
16.5 Augmenting Data
16.5.1 Image Preprocessing
16.5.2 Text Data Augmentation
16.5.2.1 Synonym Replacement
16.5.2.2 Back-Translation for NLP Applications
16.5.3 Up-Sampling and Down-Sampling
16.5.3.1 Up-Sampling
16.5.3.2 Down-Sampling
16.6 Advanced Preprocessing Techniques
16.6.1 Reduction of Dimensionality
16.6.2 Techniques for Feature Selection
16.6.3 Time–Series Data Preprocessing
16.6.3.1 Time Windowing
16.6.3.2 Seasonal Decomposition
16.6.3.3 Differencing and Trend Removal
16.6.4 Preprocessing for Voice Dataset
16.7 Case Study: Building a Preprocessing Pipeline
16.7.1 Step-by-Step Guide for a Preprocessing Pipeline
16.7.2 Implementation with Real-World Data from a Public Dataset
16.8 Best Practices in Data Preprocessing
16.8.1 Ensuring Data Consistency and Quality
16.8.2 Tips for Reproducibility and Maintaining Data Integrity
16.9 Common Challenges and Solutions in Data Preprocessing
16.9.1 Handling Noisy and Biased Data
16.9.2 Overcoming Class Imbalances
16.9.3 Automating Data Preprocessing Using Frameworks
16.10 Emerging Trends and Future Directions in Data Preprocessing
16.10.1 Generative AI for Data Augmentation
16.10.2 Self-Supervised Learning (SSL) for Reduced Dependency on Labeled Data
16.10.3 Use of Synthetic Data in Privacy-Sensitive Applications
16.11 Conclusion
References
17. Fundamentals of Machine Learning for Nanotechnology
K. Mahesh Babu, Karamsetty Shouryadhar, Sunkari Pradeep and Mahitha Dilli
17.1 Introduction
17.1.1 Machine Learning
17.1.2 Types of ML
17.1.2.1 Supervised Learning
17.1.2.2 Unsupervised Learning
17.1.2.3 Reinforcement Learning
17.1.3 Introduction to Nanotechnology
17.1.3.1 Definition and Scope of Nanotechnology
17.1.4 Intersection of ML and Nanotechnology
17.1.4.1 ML is a Powerful Tool for Nanotechnology—Details
17.1.5 Benefits of Using ML in Nanotechnology
17.1.5.1 Accelerating Research and Discovery
17.1.5.2 Enhancing Precision and Accuracy
17.1.5.3 Cutting Down Experimental Costs
17.1.5.4 Driving Innovation in Nanomedicine and Environmental Solutions
17.2 Foundations of ML for Nanotechnology
17.2.1 Types of ML Algorithms Relevant to Nanotechnology
17.2.1.1 Supervised Learning
17.2.1.2 Unsupervised Learning
17.2.1.3 Reinforcement Learning
17.2.2 Data Handling and Preprocessing in Nanotech
17.2.3 Handling Complex Datasets in Nanotechnology
17.2.3.1 Data Collection
17.2.3.2 Data Preprocessing
17.2.3.3 Normalization
17.2.3.4 Dimensionality Reduction
17.2.4 Data Augmentation Techniques to Overcome Small Sample Sizes
17.2.4.1 Image-Based Augmentation
17.2.4.2 Synthetic Data Generation
17.2.5 Generative Models
17.2.6 Benefits of Data Handling and Preprocessing in Nanotechnology ML
17.3 Key ML Techniques and Models in Nanotechnology
17.3.1 Support Vector Machines
17.3.2 Decision Trees and Random Forests
17.3.3 Neural Networks and Deep Learning
17.3.4 Applications in Nanotechnology
17.4 Clustering and Dimensionality Reduction Techniques
17.4.1 Applications in Nanotechnology
17.4.2 Applications of ML in Nanotechnology
17.4.2.1 Material Discovery and Design
17.4.2.2 Nanofabrication and Manufacturing
17.4.2.3 Characterization and Analysis
17.4.2.4 Predictive Modeling in Nanomedicine
17.5 Challenges and Future Directions in ML for Nanotechnology
17.5.1 Data Limitations and Quality
17.5.2 Model Interpretability and Validation
17.5.3 Scalability and Computational Demand
17.5.4 Ethics and Environmental Impact
17.6 Case Studies
17.6.1 ML for Discovering Novel Materials
17.6.2 ML-Assisted Nanofabrication Process Optimization
17.6.3 Predictive Modeling for Nano-Enabled Healthcare Solutions
17.7 Conclusion
References
18. Optimizing Hybrid Nanodevice Fabrication Efficiency through Unsupervised Machine Learning Approaches
Raj Kishor Verma and Udit Mamodiya
18.1 Introduction
18.1.1 Hybrid Nanodevices
18.1.2 Unsupervised Machine Learning
18.1.3 Fabrication Optimization
18.1.4 Clustering Algorithms
18.1.5 Dimensionality Reduction
18.1.6 Nanomanufacturing Efficiency
18.2 Experimental Methods and Materials/Literature Review
18.3 Proposed Diagram
18.4 Conclusion
18.5 Challenges
References
19. Emerging Trends in Micro and Nano Manufacturing: A Survey of Modern Technologies and Future Prospects
Nirmalya Pal, Shilpa Ghosh and Riya Sil
19.1 Introduction
19.1.1 Overview of IoT and AI Technologies
19.1.2 Internet of Things
19.1.3 IoT–AI Integration in Microfabrication and Nanofabrication
19.2 Literature Survey
19.3 Micromanufacturing
19.3.1 Applications of Micromanufacturing
19.3.1.1 Analysis of Applications
19.3.2 Opportunities and Challenges
19.3.2.1 Analysis of Opportunities and Challenges
19.3.3 Future Directions in Micromanufacturing
19.3.3.1 Analysis of Future Directions
19.4 Cyber Nanomanufacturing
19.4.1 Applications of CNM
19.4.1.1 Analysis of Application
19.4.2 Opportunities and Challenges
19.4.3 Applications of CNM
19.4.3.1 Analysis of Future Directions
19.5 Observational Analysis
19.5.1 Dataset Generation
19.5.2 Feature Selection
19.5.3 Linear Regression Analysis
19.5.4 Data Visualization
19.6 Conclusion
Bibliography
20. Exploring Machine Learning in Nanotechnology
Sabhyata Uppal Soni and Ahmed A. Elngar
20.1 Introduction
Literature Review
20.2 Methods for Implementing ML in Nanomaterials
20.2.1 Modeling Nanomaterial Properties Using Regression Techniques
20.2.2 Nanomaterial Functionality Assignment Algorithm
20.2.3 Decision Tree Sorting for Rank-Level Information
20.2.4 Random Forest Model for Improved Classification
20.2.5 K-Nearest Neighbors for Similarity-Based Sorting
20.3 DL for Nanomaterial Image Analysis
20.3.1 CNNs for Structural Defect Identification
20.3.2 Transfer Learning for Specialized Image Analysis
20.3.3 Autoencoders for Pattern Discovery
20.4 Optimization of Nanomaterial Synthesis Using ML
20.4.1 Bayesian Optimization for Process Parameter Tuning
20.4.2 Genetic Algorithms for Evolutionary Optimization
20.5 Challenges and Future Directions
20.5.1 Hybrid Approaches Integrating ML with Traditional Methods
20.5.2 Automated Synthesis Platforms
20.6 Modeling Properties and Behavior of Nanomaterials
20.6.1 Introduction to Nanomaterial Modeling
20.6.2 Key Properties of Nanomaterials
20.6.3 Mechanical Properties
20.6.4 Electrical Properties
20.6.5 Magnetic Properties
20.6.6 Thermal Properties
20.6.7 Chemical Properties
20.6.7.1 High Reactivity and Catalytic Activity
20.6.7.2 Chemical Stability and Reactivity
20.7 Types of Modeling Techniques in Nanotechnology
20.7.1 MD Simulations
20.7.1.1 Applications
20.7.1.2 Limitations
20.8 Density Functional Theory
20.8.1 Applications
20.8.2 Limitations
20.9 Machine Learning Models
20.9.1 Drug Delivery Systems
20.9.2 Semiconductor Devices
20.9.3 Challenges in Modeling Nanomaterials
20.9.4 Future Directions in Nanomaterial Modeling
20.10 Using DL to Analyze Nanomaterial Images
20.10.1 Application of DL with Focus on Imaging of Nanomaterials
20.10.2 Nanomaterials Imaging Techniques Commonly Used
20.10.3 Key DL Techniques for Nanomaterial Image Analysis
20.10.3.1 Convolutional Neural Networks
20.10.3.2 Autoencoders for Feature Extraction
20.10.3.3 Generative Adversarial Networks
20.10.3.4 Image Segmentation Using U-Net Models
20.11 Applications of DL in Nanomaterial Image Analysis
20.11.1 Automated Defect Detection
20.11.2 Classification of Nanomaterials
20.11.3 Predicting Material Properties
20.11.4 Super-Resolution Imaging
20.12 Challenges in Using DL for Nanomaterial Image Analysis
20.12.1 Data Scarcity and Annotation
20.12.2 Model Interpretability
20.12.3 Computational Requirements
20.12.4 Generalization Across Imaging Modalities
20.13 The Role of XAI in Nanotechnology
20.14 Conclusion
Bibliography
21. Machine Learning as a Tool in Nanodevice Fabrication
Sumaiya Samreen and Sanjeevini S. Harwalkar
21.1 Introduction
21.1.1 Overview of Nanodevices
21.1.2 Challenges in Nanodevice Fabrication
21.2 Tools Used
21.2.1 Rise of ML
21.3 Role of ML in Nanodevice Fabrication
21.3.1 Predictive Modeling
21.3.2 Process Optimization
21.3.3 Automation and Control Processing
21.4 Applications of ML in the Fabrication of Nanodevices
21.4.1 Materials Discovery
21.4.2 Nanoscale Imaging and Analyst
21.4.3 Lithography and Patterning
21.4.4 Device Performance Prediction
21.5 Advantages of ML in Nanodevice Fabrication
21.6 Challenges and Limitations
21.6.1 Data Availability and Quality
21.6.2 Computational Complexity
21.6.3 Integration with Existing Systems
21.7 Future Directions
21.7.1 Hybrid Methods: ML + Physics-Based Simulation
21.7.2 Edge Computing: Deploying Lightweight ML Models for Real-Time Control
21.7.3 Ethical Considerations: Addressing Data Security, Algorithmic Transparency, and Environmental Sustainability
21.7.4 Interdisciplinary Research: Promoting Teamwork between Data Scientists, Physicists, and Engineers
21.8 Conclusion
References
22. Optimizing Hybrid Nanodevice Fabrication Efficiency through Machine Learning: Applications in Precision Control and Defect Reduction
Sandeep Gupta and Budesh Kanwer
22.1 Introduction
22.2 The Landscape of Hybrid Nanodevice Fabrication
22.3 ML: Transforming Hybrid Nanodevice Fabrication
22.3.1 Fabrication Techniques Enhanced by ML
22.4 ML Models in Action
22.4.1 Deep Neural Networks
22.4.2 Convolutional Neural Networks
22.4.3 Reinforcement Learning
22.4.4 Synergistic Impact of ML Models
22.5 Application in Biomedical Sensors
22.6 Advancements in Semiconductor Manufacturing
22.6.1 Precision Parameter Control
22.6.2 Defect Minimization and Real-Time Detection
22.6.3 A Paradigm Shift in Hybrid Nanodevice Fabrication
22.7 Challenges in ML Applications for Semiconductor Manufacturing
22.8 Future Directions
22.8.1 Autonomous Fabrication Systems
22.8.2 Cross-Disciplinary Applications
22.9 Conclusion
References
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Enhancing Hybrid Nanodevice Fabrication Efficiency using Machine Learning