Machine Learning-Based Modelling in Atomic Layer Deposition Processes

While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology. - Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges. - Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches. - Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD. - Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues. Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications.

Oluwatobi Adeleke is a researcher at the Department of Mechanical Engineering Science, University of Johannesburg. He joined the University of Johannesburg in 2019 as a PhD researcher and completed his PhD research in 2022. He also holds a Masters' and Bachelor's degree in Mechanical Engineering from University of Ibadan and Ladoke Akintola University of Technology, respectively. His research interest is in artificial intelligence, soft computing techniques and machine learning, renewable energy, bio-energy. solar cells, waste-to-energy, and systems modeling. He has also made extensive contribution to researches in atomic layer deposition, material science, corrosion inhibition, waste management modelling and optimization and life cycle assessment. He has published articles in reputable journals neural computing and applications, Energy reports, Fuel, renewable energy, biotechnology reports, journal of material research and technology, engineered science and several other journals. He has published a number of book chapters and conference papers in these fields. He is a reviewer for several reputable journals in the field of waste management, soft computing and renewable energy. He was awarded the best PhD researcher in the Department of Mechanical Science, under the faculty of Engineering and Built Environment (FEBE) award of academic excellence, 2021. He is a member of Council for Regulation of Engineering in Nigeria (COREN), Nigeria Society of Engineers (NSE), and American Society of Mechanical Engineers (ASME). Sina Karimzadeh is a PhD research candidate at the University of Johannesburg. He holds a MSc degree in Mechanical engineering from the University of Johannesburg in 2020. He has been selected as one of the prospective for the chancellors Medal for the most meritorious Masters study for 2020. His current research interest focuses on the development of Li-ion battery active components and interface engineering by using atomic layer deposition (ALD) technique. He has also been involved in a number of projects including Hydrogen Storage, Hydrogen Generation, Thin Films and Nanotechnology, Drug Delivery, Heat Transfer, Water Purification Membrane and Computational modelling and simulation. He has published numerous journal articles and conference such as Journal of Electrochemical Energy Reviews, Journal of Energy Storage, Journal of Water Process Engineering, International Journal of Heat and Mass Transfer, Journal of Molecular Liquids, ASME International Mechanical Engineering Congress etc. He has serves as reviewer for journals such as Elsevier, Springer, etc. He is currently the Head of the ALD and innovation sub-research group and the Lead experimentalist at the ALD facility. Tien-Chien Jen is a full professor and the Head of Department, Mechanical Engineering, University of Johannesburg. Before then, Prof Jen was a faculty member at University of Wisconsin, Milwaukee. Prof Jen received his Ph.D. in Mechanical and Aerospace Engineering from UCLA, specializing in thermal aspects of grinding. He has received several competitive grants for his research, including those from the US National Science Foundation, the US Department of Energy and the EPA. He is also the Director of Manufacturing Research Centre of the University of Johannesburg. Meanwhile, SA National Research Foundation has awarded Prof Jen a NNEP grant (National Nano Equipment Program) worth of USD 1.5 million to acquire two state-of-the-art Atomic Layer Deposition (ALD) tools to be housed in a 220m2 10000 level (ISO 7) clean room facility for ultra-thin film coating. These two ALD Research facility will be the first in South Africa and possibly the first in Africa continent. He has made extensive contributions to the field of mechanical engineering, specifically in the area of machining processes, atomic layer deposition, cold gas dynamics spraying, fuel cells and hydrogen technology, batteries, and material processing. He has published several journals articles, books and book chapters in these spaces in reputable journals, and presented in several conferences.

Part 1: Introduction to Atomic Layer Deposition. 1. Overview of Atomic Layer Deposition and Thin Film Technology. 2. State of the Art Modeling and Simulation Approaches in ALD. 3. Characterization Methods in ALD. 4. Industry 4.0, Manufacturing Sector and Thin Film Technology. Part 2: Machine Learning Techniques. 5. Fundamentals of Machine Learning. 6. Supervised Learning. 7. Unsupervised Learning. 8. Deep Learning. 9. Hard and Soft Computing. Part 3: Machine Learning Applications in Atomic Layer Deposition. 10. Why Machine Learning? 11. Machine-Learning Based Predictive Analysis in ALD. 12. Machine Learning-Based Classification Techniques in ALD. 13. Deep Learning in Atomic Layer Deposition. 14. Feature Engineering in Atomic Layer Deposition. 15. Limitations, Opportunities, and Future Directions.