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.

• Preface xxv1 Challenges and Limitations in Implementation: Nanodevice Fabrication Efficiency Using Machine Learning 1Amit Kumar Jain, Tarun Mishra and Mohamed M. Awad1.1 Introduction 21.2 Related Study 41.3 Case Studies for ML-Driven Nanodevice Fabrication 51.4 Comparative Study between Challenges and Limitations in Hybrid Nanodevice Fabrication Efficiency Using ML 81.5 Applications 111.6 Advantages of ML in Hybrid Nanodevice Fabrication Efficiency 151.7 Disadvantages of ML in Hybrid Nanodevice Fabrication Efficiency 161.8 Future Scope 181.9 Conclusion 202 A Comprehensive Review of Machine Learning Algorithms and their Utilization in Nanodevice Fabrication 23Basudha Dewan2.1 Introduction 242.2 Universal ML Model 252.3 Types of ML Algorithms 272.4 Challenges in ML 322.5 Recent Developments in ML 332.6 Ethical Concerns and Fairness in ML 332.7 Role of ML in Nanodevice Fabrication 332.8 Proposed Model 352.9 Conclusion 363 Integrating Deep Learning in Rolling Process Design for Nanocomposites: A Novel Approach to Strength Prediction 41Amit Tiwari, Payal Bansal, Rachid Amrousse and SeitkhanAzat3.1 Introduction 423.2 Database Collection 463.3 Computational Modeling 463.4 Results and Discussion 493.5 Conclusion 594 Future Directions in Machine Learning–Driven Nanodevice Fabrication 63Wasswa Shafik4.1 Introduction 644.2 Fundamentals of Nanodevice Fabrication 654.3 ML Techniques in Nanodevice Fabrication 704.4 Applications of ML in Nanodevice Fabrication 774.5 Challenges and Limitations 804.6 Future Research Directions 834.7 Conclusion 895 Unlocking Machine Learning: Revolutionizing Fabrication of Nanocircuitry 93Mohammed Firdos Alam Sheikh, Nikhil Kumar Goyal, Udit Mamodiya and Tien Anh Tran6 Enabling Smarter Nanosystems: The Role of AI and Supervised Machine Learning in Nanotechnology 113Indra Kishor, Udit Mamodiya, Sayed Sayeed Ahmad, Priya Goyal and Deepti Dwivedi6.1 Introduction 1146.2 Literature Review 1166.3 Methodology 1226.4 Results 1286.5 Discussion 1316.6 Conclusion 1337 Harnessing Unsupervised Machine Learning for Advanced Nanodevice Fabrication 139Indra Kishor, Udit Mamodiya, Sayed Sayeed Ahmad, Priya Goyal and Deepti Dwivedi7.1 Introduction 1407.2 Literature Review 1417.3 Methodology 1437.4 Results 1467.5 Discussion 1537.6 Conclusion 1558 Supervised Learning Models for Fabrication Optimization in Semiconductor Nanodevices 159Irfan Ahmad Pindoo and Suman Lata Tripathi8.1 Introduction 1608.2 The Semiconductor Industry and Machine Learning 1638.3 Semiconductor Fabrication Process 1648.4 Applications of Supervised Learning in Fabrication Optimization 1688.5 Machine Learning–Based Semiconductor Process Optimization 1709 Advancements and Challenges in Nanomaterial Integration for Next-Generation Devices 179Mukesh Chand, Pooja Rani, Charul Bapna and Garima Kachhara9.1 Introduction 1809.2 Nanomaterials in Device Integration 1849.3 Related Work 1889.4 Fabrication Techniques for Nanomaterial Integration 1899.5 Challenges in Nanomaterial Integration 1939.6 Conclusions and Future Directions 19410 An Efficient Exploration of Process Optimization through Deep Learning Approaches 197Nikhil Kumar Goyal, Monika Dandotiya, Monika Kumari, Shikha Sharma and A. Anushya10.1 Introduction 19810.2 Deep Learning Architectures for Process Optimization 20910.3 Challenges and Limitations in the Deep Learning Process Optimization Process 21110.4 Conclusion 21411 Machine Learning Approach for Quantum Dots Synthesis 219Rajat Kumar Goyal, Nidhi Bharadwaj and Pramod Garhwal11.1 Introduction 22011.2 Basic and Operating Principles of ML 22111.3 Various ML Algorithms for QD Research 22311.4 Summary and Future Perspectives 23112 Deep Learning for Process Optimization: Techniques, Applications, and Future Directions 239Randhir Singh Baghel, Bindiya Jain, Udit Mamodiya and Harkaran Singh12.1 Introduction 24012.2 Overview of Process Optimization 24112.3 Role of DL in Optimization 24312.4 Optimization in Industrial and Business Contexts 24512.5 Applications of DL in Process Optimization 24612.6 Deep Learning Applications in Supply Chain and Logistics Optimization 24812.7 Challenges in Implementing DL for Process Optimization 25413 Advanced ML Algorithms for Nanotechnology 259R. Remya, Shaik Saniya, O. Jeba Singh and Umesh Sampath13.1 Introduction 26013.2 Deep Learning for Nanoscale Imaging 26213.3 Graph Neural Networks for Molecular Structure 26413.4 Quantum ML for Nanotechnology Applications 26913.5 RL in Nanofabrication 27013.6 Meta Learning for Metal Discovery 27113.7 Conclusion 27214 Integrating Machine Learning and Nanotechnology: Driving Innovation and Sustainable Solutions 275Shruti Gupta, Sourabh Kumar Jain and Gireesh Kumar14.1 Introduction 27614.2 Steps Involved in Building an ML Model 28014.3 How AI and Nanotechnology are Revolutionizing Healthcare and Safety 28414.4 Ensuring Quality in Nanomanufacturing 28514.5 Environmental Monitoring and Remediation 28614.6 Advancements in Nanotechnology and Quantum Computing 28714.7 AI and Nanotechnology: Challenges and Future Opportunities 28914.8 Conclusion 28915 Case Studies in ML-Driven AI Nanodevice Fabrication 293Yogita Thareja, Sakshi Khullar and Parulpreet Singh15.1 Introduction 29415.2 Experimental Survey and Materials 29515.3 Methodology 29715.4 Results 30315.5 Conclusion 30516 Data Acquisition and Preprocessing Techniques for Effective Machine Learning 311B. Sarada, C. Gazala Akhtar, N. Shaleen Saroj and Sanjeevini S. Harwalka16.1 Introduction 31216.2 Data Acquisition—Definition and Role in ML 31416.3 Data Cleaning 31816.4 Data Transformation 32116.5 Augmenting Data 32416.6 Advanced Preprocessing Techniques 32816.7 Case Study: Building a Preprocessing Pipeline 33116.8 Best Practices in Data Preprocessing 33416.9 Common Challenges and Solutions in Data Preprocessing 33516.10 Emerging Trends and Future Directions in Data Preprocessing 33616.11 Conclusion 33717 Fundamentals of Machine Learning for Nanotechnology 341K. Mahesh Babu, Karamsetty Shouryadhar, Sunkari Pradeep and Mahitha Dilli17.1 Introduction 34217.2 Foundations of ML for Nanotechnology 34717.3 Key ML Techniques and Models in Nanotechnology 35117.4 Clustering and Dimensionality Reduction Techniques 35317.5 Challenges and Future Directions in ML for Nanotechnology 35517.6 Case Studies 35717.7 Conclusion 36018 Optimizing Hybrid Nanodevice Fabrication Efficiency through Unsupervised Machine Learning Approaches 363Raj Kishor Verma and Udit Mamodiya18.1 Introduction 36418.2 Experimental Methods and Materials/Literature Review 37418.3 Proposed Diagram 37418.4 Conclusion 37918.5 Challenges 38019 Emerging Trends in Micro and Nano Manufacturing: A Survey of Modern Technologies and Future Prospects 383Nirmalya Pal, Shilpa Ghosh and Riya Sil19.1 Introduction 38419.2 Literature Survey 38619.3 Micromanufacturing 38719.4 Cyber Nanomanufacturing 39619.5 Observational Analysis 39819.6 Conclusion 40120 Exploring Machine Learning in Nanotechnology 405Sabhyata Uppal Soni and Ahmed A. Elngar20.1 Introduction 40620.2 Methods for Implementing ML in Nanomaterials 40820.3 DL for Nanomaterial Image Analysis 40920.4 Optimization of Nanomaterial Synthesis Using ML 41020.5 Challenges and Future Directions 41120.6 Modeling Properties and Behavior of Nanomaterials 41120.7 Types of Modeling Techniques in Nanotechnology 41420.8 Density Functional Theory 41520.9 Machine Learning Models 41520.10 Using DL to Analyze Nanomaterial Images 41720.11 Applications of DL in Nanomaterial Image Analysis 42020.12 Challenges in Using DL for Nanomaterial Image Analysis 42120.13 The Role of XAI in Nanotechnology 42220.14 Conclusion 42321 Machine Learning as a Tool in Nanodevice Fabrication 425Sumaiya Samreen and Sanjeevini S. Harwalkar21.1 Introduction 42521.2 Tools Used 42721.3 Role of ML in Nanodevice Fabrication 42821.4 Applications of ML in the Fabrication of Nanodevices 43021.5 Advantages of ML in Nanodevice Fabrication 43321.6 Challenges and Limitations 43521.7 Future Directions 43821.8 Conclusion 44022 Optimizing Hybrid Nanodevice Fabrication Efficiency through Machine Learning: Applications in Precision Control and Defect Reduction 443Sandeep Gupta and Budesh Kanwer22.1 Introduction 44422.2 THe Landscape of Hybrid Nanodevice Fabrication 44522.3 ML: Transforming Hybrid Nanodevice Fabrication 44722.4 ML Models in Action 44722.5 Application in Biomedical Sensors 45022.6 Advancements in Semiconductor Manufacturing 45122.7 Challenges in ML Applications for Semiconductor Manufacturing 45322.8 Future Directions 45422.9 Conclusion 455References 456Index 459

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.

• Preface xxv1 Challenges and Limitations in Implementation: Nanodevice Fabrication Efficiency Using Machine Learning 1Amit Kumar Jain, Tarun Mishra and Mohamed M. Awad1.1 Introduction 21.2 Related Study 41.3 Case Studies for ML-Driven Nanodevice Fabrication 51.4 Comparative Study between Challenges and Limitations in Hybrid Nanodevice Fabrication Efficiency Using ML 81.5 Applications 111.6 Advantages of ML in Hybrid Nanodevice Fabrication Efficiency 151.7 Disadvantages of ML in Hybrid Nanodevice Fabrication Efficiency 161.8 Future Scope 181.9 Conclusion 202 A Comprehensive Review of Machine Learning Algorithms and their Utilization in Nanodevice Fabrication 23Basudha Dewan2.1 Introduction 242.2 Universal ML Model 252.3 Types of ML Algorithms 272.4 Challenges in ML 322.5 Recent Developments in ML 332.6 Ethical Concerns and Fairness in ML 332.7 Role of ML in Nanodevice Fabrication 332.8 Proposed Model 352.9 Conclusion 363 Integrating Deep Learning in Rolling Process Design for Nanocomposites: A Novel Approach to Strength Prediction 41Amit Tiwari, Payal Bansal, Rachid Amrousse and SeitkhanAzat3.1 Introduction 423.2 Database Collection 463.3 Computational Modeling 463.4 Results and Discussion 493.5 Conclusion 594 Future Directions in Machine Learning–Driven Nanodevice Fabrication 63Wasswa Shafik4.1 Introduction 644.2 Fundamentals of Nanodevice Fabrication 654.3 ML Techniques in Nanodevice Fabrication 704.4 Applications of ML in Nanodevice Fabrication 774.5 Challenges and Limitations 804.6 Future Research Directions 834.7 Conclusion 895 Unlocking Machine Learning: Revolutionizing Fabrication of Nanocircuitry 93Mohammed Firdos Alam Sheikh, Nikhil Kumar Goyal, Udit Mamodiya and Tien Anh Tran6 Enabling Smarter Nanosystems: The Role of AI and Supervised Machine Learning in Nanotechnology 113Indra Kishor, Udit Mamodiya, Sayed Sayeed Ahmad, Priya Goyal and Deepti Dwivedi6.1 Introduction 1146.2 Literature Review 1166.3 Methodology 1226.4 Results 1286.5 Discussion 1316.6 Conclusion 1337 Harnessing Unsupervised Machine Learning for Advanced Nanodevice Fabrication 139Indra Kishor, Udit Mamodiya, Sayed Sayeed Ahmad, Priya Goyal and Deepti Dwivedi7.1 Introduction 1407.2 Literature Review 1417.3 Methodology 1437.4 Results 1467.5 Discussion 1537.6 Conclusion 1558 Supervised Learning Models for Fabrication Optimization in Semiconductor Nanodevices 159Irfan Ahmad Pindoo and Suman Lata Tripathi8.1 Introduction 1608.2 The Semiconductor Industry and Machine Learning 1638.3 Semiconductor Fabrication Process 1648.4 Applications of Supervised Learning in Fabrication Optimization 1688.5 Machine Learning–Based Semiconductor Process Optimization 1709 Advancements and Challenges in Nanomaterial Integration for Next-Generation Devices 179Mukesh Chand, Pooja Rani, Charul Bapna and Garima Kachhara9.1 Introduction 1809.2 Nanomaterials in Device Integration 1849.3 Related Work 1889.4 Fabrication Techniques for Nanomaterial Integration 1899.5 Challenges in Nanomaterial Integration 1939.6 Conclusions and Future Directions 19410 An Efficient Exploration of Process Optimization through Deep Learning Approaches 197Nikhil Kumar Goyal, Monika Dandotiya, Monika Kumari, Shikha Sharma and A. Anushya10.1 Introduction 19810.2 Deep Learning Architectures for Process Optimization 20910.3 Challenges and Limitations in the Deep Learning Process Optimization Process 21110.4 Conclusion 21411 Machine Learning Approach for Quantum Dots Synthesis 219Rajat Kumar Goyal, Nidhi Bharadwaj and Pramod Garhwal11.1 Introduction 22011.2 Basic and Operating Principles of ML 22111.3 Various ML Algorithms for QD Research 22311.4 Summary and Future Perspectives 23112 Deep Learning for Process Optimization: Techniques, Applications, and Future Directions 239Randhir Singh Baghel, Bindiya Jain, Udit Mamodiya and Harkaran Singh12.1 Introduction 24012.2 Overview of Process Optimization 24112.3 Role of DL in Optimization 24312.4 Optimization in Industrial and Business Contexts 24512.5 Applications of DL in Process Optimization 24612.6 Deep Learning Applications in Supply Chain and Logistics Optimization 24812.7 Challenges in Implementing DL for Process Optimization 25413 Advanced ML Algorithms for Nanotechnology 259R. Remya, Shaik Saniya, O. Jeba Singh and Umesh Sampath13.1 Introduction 26013.2 Deep Learning for Nanoscale Imaging 26213.3 Graph Neural Networks for Molecular Structure 26413.4 Quantum ML for Nanotechnology Applications 26913.5 RL in Nanofabrication 27013.6 Meta Learning for Metal Discovery 27113.7 Conclusion 27214 Integrating Machine Learning and Nanotechnology: Driving Innovation and Sustainable Solutions 275Shruti Gupta, Sourabh Kumar Jain and Gireesh Kumar14.1 Introduction 27614.2 Steps Involved in Building an ML Model 28014.3 How AI and Nanotechnology are Revolutionizing Healthcare and Safety 28414.4 Ensuring Quality in Nanomanufacturing 28514.5 Environmental Monitoring and Remediation 28614.6 Advancements in Nanotechnology and Quantum Computing 28714.7 AI and Nanotechnology: Challenges and Future Opportunities 28914.8 Conclusion 28915 Case Studies in ML-Driven AI Nanodevice Fabrication 293Yogita Thareja, Sakshi Khullar and Parulpreet Singh15.1 Introduction 29415.2 Experimental Survey and Materials 29515.3 Methodology 29715.4 Results 30315.5 Conclusion 30516 Data Acquisition and Preprocessing Techniques for Effective Machine Learning 311B. Sarada, C. Gazala Akhtar, N. Shaleen Saroj and Sanjeevini S. Harwalka16.1 Introduction 31216.2 Data Acquisition—Definition and Role in ML 31416.3 Data Cleaning 31816.4 Data Transformation 32116.5 Augmenting Data 32416.6 Advanced Preprocessing Techniques 32816.7 Case Study: Building a Preprocessing Pipeline 33116.8 Best Practices in Data Preprocessing 33416.9 Common Challenges and Solutions in Data Preprocessing 33516.10 Emerging Trends and Future Directions in Data Preprocessing 33616.11 Conclusion 33717 Fundamentals of Machine Learning for Nanotechnology 341K. Mahesh Babu, Karamsetty Shouryadhar, Sunkari Pradeep and Mahitha Dilli17.1 Introduction 34217.2 Foundations of ML for Nanotechnology 34717.3 Key ML Techniques and Models in Nanotechnology 35117.4 Clustering and Dimensionality Reduction Techniques 35317.5 Challenges and Future Directions in ML for Nanotechnology 35517.6 Case Studies 35717.7 Conclusion 36018 Optimizing Hybrid Nanodevice Fabrication Efficiency through Unsupervised Machine Learning Approaches 363Raj Kishor Verma and Udit Mamodiya18.1 Introduction 36418.2 Experimental Methods and Materials/Literature Review 37418.3 Proposed Diagram 37418.4 Conclusion 37918.5 Challenges 38019 Emerging Trends in Micro and Nano Manufacturing: A Survey of Modern Technologies and Future Prospects 383Nirmalya Pal, Shilpa Ghosh and Riya Sil19.1 Introduction 38419.2 Literature Survey 38619.3 Micromanufacturing 38719.4 Cyber Nanomanufacturing 39619.5 Observational Analysis 39819.6 Conclusion 40120 Exploring Machine Learning in Nanotechnology 405Sabhyata Uppal Soni and Ahmed A. Elngar20.1 Introduction 40620.2 Methods for Implementing ML in Nanomaterials 40820.3 DL for Nanomaterial Image Analysis 40920.4 Optimization of Nanomaterial Synthesis Using ML 41020.5 Challenges and Future Directions 41120.6 Modeling Properties and Behavior of Nanomaterials 41120.7 Types of Modeling Techniques in Nanotechnology 41420.8 Density Functional Theory 41520.9 Machine Learning Models 41520.10 Using DL to Analyze Nanomaterial Images 41720.11 Applications of DL in Nanomaterial Image Analysis 42020.12 Challenges in Using DL for Nanomaterial Image Analysis 42120.13 The Role of XAI in Nanotechnology 42220.14 Conclusion 42321 Machine Learning as a Tool in Nanodevice Fabrication 425Sumaiya Samreen and Sanjeevini S. Harwalkar21.1 Introduction 42521.2 Tools Used 42721.3 Role of ML in Nanodevice Fabrication 42821.4 Applications of ML in the Fabrication of Nanodevices 43021.5 Advantages of ML in Nanodevice Fabrication 43321.6 Challenges and Limitations 43521.7 Future Directions 43821.8 Conclusion 44022 Optimizing Hybrid Nanodevice Fabrication Efficiency through Machine Learning: Applications in Precision Control and Defect Reduction 443Sandeep Gupta and Budesh Kanwer22.1 Introduction 44422.2 THe Landscape of Hybrid Nanodevice Fabrication 44522.3 ML: Transforming Hybrid Nanodevice Fabrication 44722.4 ML Models in Action 44722.5 Application in Biomedical Sensors 45022.6 Advancements in Semiconductor Manufacturing 45122.7 Challenges in ML Applications for Semiconductor Manufacturing 45322.8 Future Directions 45422.9 Conclusion 455References 456Index 459