Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning
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.
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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Persons
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.
Content
Preface xxv
1 Challenges and Limitations in Implementation: Nanodevice Fabrication Efficiency Using Machine Learning 1
Amit Kumar Jain, Tarun Mishra and Mohamed M. Awad
1.1 Introduction 2
1.2 Related Study 4
1.3 Case Studies for ML-Driven Nanodevice Fabrication 5
1.4 Comparative Study between Challenges and Limitations in Hybrid Nanodevice Fabrication Efficiency Using ML 8
1.5 Applications 11
1.6 Advantages of ML in Hybrid Nanodevice Fabrication Efficiency 15
1.7 Disadvantages of ML in Hybrid Nanodevice Fabrication Efficiency 16
1.8 Future Scope 18
1.9 Conclusion 20
2 A Comprehensive Review of Machine Learning Algorithms and their Utilization in Nanodevice Fabrication 23
Basudha Dewan
2.1 Introduction 24
2.2 Universal ML Model 25
2.3 Types of ML Algorithms 27
2.4 Challenges in ML 32
2.5 Recent Developments in ML 33
2.6 Ethical Concerns and Fairness in ML 33
2.7 Role of ML in Nanodevice Fabrication 33
2.8 Proposed Model 35
2.9 Conclusion 36
3 Integrating Deep Learning in Rolling Process Design for Nanocomposites: A Novel Approach to Strength Prediction 41
Amit Tiwari, Payal Bansal, Rachid Amrousse and SeitkhanAzat
3.1 Introduction 42
3.2 Database Collection 46
3.3 Computational Modeling 46
3.4 Results and Discussion 49
3.5 Conclusion 59
4 Future Directions in Machine Learning-Driven Nanodevice Fabrication 63
Wasswa Shafik
4.1 Introduction 64
4.2 Fundamentals of Nanodevice Fabrication 65
4.3 ML Techniques in Nanodevice Fabrication 70
4.4 Applications of ML in Nanodevice Fabrication 77
4.5 Challenges and Limitations 80
4.6 Future Research Directions 83
4.7 Conclusion 89
5 Unlocking Machine Learning: Revolutionizing Fabrication of Nanocircuitry 93
Mohammed Firdos Alam Sheikh, Nikhil Kumar Goyal, Udit Mamodiya and Tien Anh Tran
6 Enabling Smarter Nanosystems: The Role of AI and Supervised Machine Learning in Nanotechnology 113
Indra Kishor, Udit Mamodiya, Sayed Sayeed Ahmad, Priya Goyal and Deepti Dwivedi
6.1 Introduction 114
6.2 Literature Review 116
6.3 Methodology 122
6.4 Results 128
6.5 Discussion 131
6.6 Conclusion 133
7 Harnessing Unsupervised Machine Learning for Advanced Nanodevice Fabrication 139
Indra Kishor, Udit Mamodiya, Sayed Sayeed Ahmad, Priya Goyal and Deepti Dwivedi
7.1 Introduction 140
7.2 Literature Review 141
7.3 Methodology 143
7.4 Results 146
7.5 Discussion 153
7.6 Conclusion 155
8 Supervised Learning Models for Fabrication Optimization in Semiconductor Nanodevices 159
Irfan Ahmad Pindoo and Suman Lata Tripathi
8.1 Introduction 160
8.2 The Semiconductor Industry and Machine Learning 163
8.3 Semiconductor Fabrication Process 164
8.4 Applications of Supervised Learning in Fabrication Optimization 168
8.5 Machine Learning-Based Semiconductor Process Optimization 170
9 Advancements and Challenges in Nanomaterial Integration for Next-Generation Devices 179
Mukesh Chand, Pooja Rani, Charul Bapna and Garima Kachhara
9.1 Introduction 180
9.2 Nanomaterials in Device Integration 184
9.3 Related Work 188
9.4 Fabrication Techniques for Nanomaterial Integration 189
9.5 Challenges in Nanomaterial Integration 193
9.6 Conclusions and Future Directions 194
10 An Efficient Exploration of Process Optimization through Deep Learning Approaches 197
Nikhil Kumar Goyal, Monika Dandotiya, Monika Kumari, Shikha Sharma and A. Anushya
10.1 Introduction 198
10.2 Deep Learning Architectures for Process Optimization 209
10.3 Challenges and Limitations in the Deep Learning Process Optimization Process 211
10.4 Conclusion 214
11 Machine Learning Approach for Quantum Dots Synthesis 219
Rajat Kumar Goyal, Nidhi Bharadwaj and Pramod Garhwal
11.1 Introduction 220
11.2 Basic and Operating Principles of ML 221
11.3 Various ML Algorithms for QD Research 223
11.4 Summary and Future Perspectives 231
12 Deep Learning for Process Optimization: Techniques, Applications, and Future Directions 239
Randhir Singh Baghel, Bindiya Jain, Udit Mamodiya and Harkaran Singh
12.1 Introduction 240
12.2 Overview of Process Optimization 241
12.3 Role of DL in Optimization 243
12.4 Optimization in Industrial and Business Contexts 245
12.5 Applications of DL in Process Optimization 246
12.6 Deep Learning Applications in Supply Chain and Logistics Optimization 248
12.7 Challenges in Implementing DL for Process Optimization 254
13 Advanced ML Algorithms for Nanotechnology 259
R. Remya, Shaik Saniya, O. Jeba Singh and Umesh Sampath
13.1 Introduction 260
13.2 Deep Learning for Nanoscale Imaging 262
13.3 Graph Neural Networks for Molecular Structure 264
13.4 Quantum ML for Nanotechnology Applications 269
13.5 RL in Nanofabrication 270
13.6 Meta Learning for Metal Discovery 271
13.7 Conclusion 272
14 Integrating Machine Learning and Nanotechnology: Driving Innovation and Sustainable Solutions 275
Shruti Gupta, Sourabh Kumar Jain and Gireesh Kumar
14.1 Introduction 276
14.2 Steps Involved in Building an ML Model 280
14.3 How AI and Nanotechnology are Revolutionizing Healthcare and Safety 284
14.4 Ensuring Quality in Nanomanufacturing 285
14.5 Environmental Monitoring and Remediation 286
14.6 Advancements in Nanotechnology and Quantum Computing 287
14.7 AI and Nanotechnology: Challenges and Future Opportunities 289
14.8 Conclusion 289
15 Case Studies in ML-Driven AI Nanodevice Fabrication 293
Yogita Thareja, Sakshi Khullar and Parulpreet Singh
15.1 Introduction 294
15.2 Experimental Survey and Materials 295
15.3 Methodology 297
15.4 Results 303
15.5 Conclusion 305
16 Data Acquisition and Preprocessing Techniques for Effective Machine Learning 311
B. Sarada, C. Gazala Akhtar, N. Shaleen Saroj and Sanjeevini S. Harwalka
16.1 Introduction 312
16.2 Data Acquisition-Definition and Role in ML 314
16.3 Data Cleaning 318
16.4 Data Transformation 321
16.5 Augmenting Data 324
16.6 Advanced Preprocessing Techniques 328
16.7 Case Study: Building a Preprocessing Pipeline 331
16.8 Best Practices in Data Preprocessing 334
16.9 Common Challenges and Solutions in Data Preprocessing 335
16.10 Emerging Trends and Future Directions in Data Preprocessing 336
16.11 Conclusion 337
17 Fundamentals of Machine Learning for Nanotechnology 341
K. Mahesh Babu, Karamsetty Shouryadhar, Sunkari Pradeep and Mahitha Dilli
17.1 Introduction 342
17.2 Foundations of ML for Nanotechnology 347
17.3 Key ML Techniques and Models in Nanotechnology 351
17.4 Clustering and Dimensionality Reduction Techniques 353
17.5 Challenges and Future Directions in ML for Nanotechnology 355
17.6 Case Studies 357
17.7 Conclusion 360
18 Optimizing Hybrid Nanodevice Fabrication Efficiency through Unsupervised Machine Learning Approaches 363
Raj Kishor Verma and Udit Mamodiya
18.1 Introduction 364
18.2 Experimental Methods and Materials/Literature Review 374
18.3 Proposed Diagram 374
18.4 Conclusion 379
18.5 Challenges 380
19 Emerging Trends in Micro and Nano Manufacturing: A Survey of Modern Technologies and Future Prospects 383
Nirmalya Pal, Shilpa Ghosh and Riya Sil
19.1 Introduction 384
19.2 Literature Survey 386
19.3 Micromanufacturing 387
19.4 Cyber Nanomanufacturing 396
19.5 Observational Analysis 398
19.6 Conclusion 401
20 Exploring Machine Learning in Nanotechnology 405
Sabhyata Uppal Soni and Ahmed A. Elngar
20.1 Introduction 406
20.2 Methods for Implementing ML in Nanomaterials 408
20.3 DL for Nanomaterial Image Analysis 409
20.4 Optimization of Nanomaterial Synthesis Using ML 410
20.5 Challenges and Future Directions 411
20.6 Modeling Properties and Behavior of Nanomaterials 411
20.7 Types of Modeling Techniques in Nanotechnology 414
20.8 Density Functional Theory 415
20.9 Machine Learning Models 415
20.10 Using DL to Analyze Nanomaterial Images 417
20.11 Applications of DL in Nanomaterial Image Analysis 420
20.12 Challenges in Using DL for Nanomaterial Image Analysis 421
20.13 The Role of XAI in Nanotechnology 422
20.14 Conclusion 423
21 Machine Learning as a Tool in Nanodevice Fabrication 425
Sumaiya Samreen and Sanjeevini S. Harwalkar
21.1 Introduction 425
21.2 Tools Used 427
21.3 Role of ML in Nanodevice Fabrication 428
21.4 Applications of ML in the Fabrication of Nanodevices 430
21.5 Advantages of ML in Nanodevice Fabrication 433
21.6 Challenges and Limitations 435
21.7 Future Directions 438
21.8 Conclusion 440
22 Optimizing Hybrid Nanodevice Fabrication Efficiency through Machine Learning: Applications in Precision Control and Defect Reduction 443
Sandeep Gupta and Budesh Kanwer
22.1 Introduction 444
22.2 THe Landscape of Hybrid Nanodevice Fabrication 445
22.3 ML: Transforming Hybrid Nanodevice Fabrication 447
22.4 ML Models in Action 447
22.5 Application in Biomedical Sensors 450
22.6 Advancements in Semiconductor Manufacturing 451
22.7 Challenges in ML Applications for Semiconductor Manufacturing 453
22.8 Future Directions 454
22.9 Conclusion 455
References 456
Index 459
1
Challenges and Limitations in Implementation: Nanodevice Fabrication Efficiency Using Machine Learning
Amit Kumar Jain1*, Tarun Mishra1 and Mohamed M. Awad2
1Dept. of Electrical & Electronics Engineering, Poornima University, Rajasthan, India
2Mechanical Power Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Abstract
This chapter thus provides a brief summary of the critical issues and constraints relating to the integration of machine learning (ML) in the production of hybrid nanodevices and the technical and business difficulties that surround the mainstreaming of the technology. One of the primary issues with ML application in nanodevice fabrication is that the data used are often low quality and limited in availability. Nanofabrication is a sophisticated process that is characterized by variations in characteristics of materials and conditions, which allow a huge volume of high-quality data to be collected for model training for ML. For effectiveness of procedures used in fabrication, minimum accuracy comes from noisy or inadequate data, which leads to wrong predictions. Additionally, the computational costs remain high for higher-level algorithms; more so, the deep learning algorithms demand significant computing power for the handling of simulations as well as large dataset. This is because by increasing the cost of the production process, it becomes difficult for research laboratories and manufacturing facilities to purchase these technologies for extensive use. Nevertheless, the future of ML in hybrid nanodevice fabrication remains possibly challenging but promising. Some of the current limitations can be handled using transfer learning, edge computing, and self-learning systems, among others. Moreover, due to improvements in nanotechnology and ML, data generation will be more effective as well as computational approaches, and integration with the existing environments will be less challenging.
Keywords: Machine learning (ML), hybrid nanodevices, nanofabrication, data quality, model interpretability, deep learning, computational costs
1.1 Introduction
1.1.1 Overview of Hybrid Nanodevice Fabrication
Nanotechnology at present has achieved fast progress and has thereby prompted the emergence of enhanced nanodevices and nanoscale devices that possess new characteristics in physics, chemistry, and even biology. Nanoscale electrical sensors are nowadays the backbone of the most electrical products, including circuits, energy systems, and medical and environmental applications because of their sensitivity, selectivity, and scale integration. Novel nanoscale devices that can consist of at least two materials and provide at least two functionalities are especially interesting because of multiple benefits such as their efficiency. For example, within electronics, there may be a synergism where a hybrid nanodevice contains both semiconductor material with conductive polymers that are used in making superior sensors or transistors. The biomedical standing of these composites gets seen in metal-polymer hybrid nanostructures working toward better drug delivery technologies with controllable release capabilities.
1.1.2 Role of Machine Learning in Nanodevice Fabrication
Machine learning (ML) provides a revolutionary solution to these fabrication challenges as it will improve fabrication efficiency, accuracy, and scale. Because ML algorithms follow the data patterns to analyze and learn the likely outcomes, fabrication parameters can be modeled using ML to predict data results that can simplify fabrication processes. Conventional manufacturing techniques rely on modifying the fabrication conditions "by guess and by golly" a slow and expensive way of doing things that also has fixed boundaries to improvement. Specifically, although artificial neural networks can be trained using the large banks of materials properties and fabrication conditions, the successful outcome can be well dictated with high accuracy, and often the identification of optimal fabrication parameter combination for given devices' characteristics is possible [1-3].
ML applications in nanodevice fabrication can be broadly categorized into several areas:
- Predictive modeling: Experimental characterization can then be minimized so that through the input of parameters into the predictive ML models, various material properties and device functions can be predicted at design stage. For example, ML can predict how a specific material will perform when subjected to certain fabrication conditions, such as stress or temperature, for example, which help to speed up material selection and design.
- Process control and optimization: Using real-time ML-based monitoring systems fabricators can change fabrication parameters instantly to ensure that all of the produced devices have similar characteristics and minimal variability. It is particularly helpful in the processes such as chemical vapor deposition or etching because environmental conditions influence outcomes.
- Failure detection and quality control: One of the ways that the company is using the unused ML algorithms is in detecting defects at the initial stages of the fabrication process, hence minimizing wastage while producing high-standard products. Computer vision for instance can be coupled with ML to inspect imaging data from scanning electron microscopy (SEM) or atomic force microscopy and alert of imperfections that can affect the functionality of the devices.
The implementation of an ML approach to the manufacturing of nanodevices has potential. Not only does it enable refinement in device production but also speeds up the generation process leading to new possibilities. For instance, if it accelerated the design and manufacture of even more sensitive biosensors, ML could be the force behind the rapid development of personalized medicine. Likewise, in sustainable energy using artificial intelligence (AI) technology, some ML-facilitated fabrication techniques are likely to result in increased efficiency of solar panels or batteries [4].
1.1.3 Scope of the Chapter
The primary concerns and constraints of this study concerned the use of ML in enhancing fabrication efficiency of hybrid nanodevice. ML challenges mentioned involve the challenges within the hybrid nanostructures, lack of high quality data, and complexity of the high-end sophisticated models. We will also discuss the current solutions to these limitations that are being applied at the moment, which include development of better algorithms and building data sharing strategies, as well as integration of ML with other approaches. Last but not least, we will discuss the future of this field, where ideas of quantum computing will be implemented, setting up self-fabrication systems, as well as the future of ML in the materials discovery process.
1.2 Related Study
Huang et al. (2021): Machine learning-driven predictive modeling for nanomaterial synthesis also uses ML models for the prediction of the nanomaterial characteristics from the synthesis parameters. Through use of datasets of the different hybrid nanostructures, the paper underscores how the use of the ML method cuts on the time needed to choose materials as well as the number of trials needed [1].
Liu et al. (2021): In another case, the authors' team applied reinforcement learning for the thin-film deposition, fixing the rates of deposition in order to achieve the high thin-film uniformity. This approach increased the fabrication accuracy by 18% and demonstrated the ability of ML for adaptive control in intricate procedures [2].
Chowdhury et al. (2021) specifically compare the application of data-driven quality control in nanofabrication based on convolutional neural network (CNN) detection for manufacturing defect in nanodevice. From the analysis of SEM images, the model proved to have a high accuracy in defect segmentation, which can enhance quality control using ML in real time [4].
Mitra and Hassan (2022): Issues and prospects of data acquisition for ML in nanotechnology raise issues on availability of data used in ML-based nanotechnology research and the need for policy standardization. Two, they recommend that a framework should be developed that will trigger the swapping of data among different laboratories and materials to enhance the performance of the ML models [5].
Ramirez et al. (2022): "Hybrid Material Design Through Generative Adversarial Networks (GANs)" proposed a method based on GAN for designing novel hybrid nanomaterials. The model discovered material combinations that this paper has not encountered before, proving the applicability of ML in material design [6].
Tan et al. (2022): In the research effort titled, "Bayesian Optimization for Efficient Design of Micro/Nano Systems: Improving Fabrication Yield in Nanodevices Using Bayesian Optimization," models were created to predict the best fabrication parameter settings The result was yield enhancement for the hybrid nanotransistors that was at least 15% more consistent. This method has potential to be applied in improving efficiency of manufacturing lines [7].
Nakamura et al. (2022): Control of nanodevice processes through interpretable ML models provides an overview of the sample design and interpretability issues in deep learning...
