Название: Machine Learning-Based Modelling in Atomic Layer Deposition Processes
Автор: Oluwatobi Adeleke, Tien-Chien Jen, Sina Karimzadeh
Издательство: CRC Press
Год: 2024
Страниц: 377
Язык: английский
Формат: pdf (true)
Размер: 40.2 MB

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.

This book covers the current research in these two emerging spaces. It targets researchers, engineers, and students interested in ALD modeling Machine Learning algorithm creation and implementation. There are three sections in the book and 15 chapters in all. The first section introduces the fundamentals of ALD and thin film technology. The chapters in this section address ALD and thin film overview, state-of-the-art modeling and simulation approaches in ALD, ALD characterization methods, and industry 4.0 in thin film technology. The second section discusses the fundamentals of Machine Learning. The chapters in this section discuss Machine Learning techniques and algorithms, applications, challenges and limitations, and methods of optimal model development. Specifically, Chapters 6–8 discuss supervised learning, unsupervised learning, and Deep Learning, respectively, while Chapter 9 examines the hard and soft computing techniques. The third section explores various applications of machine learning-based ALD modeling, ranging from simple predictive analysis and classification tasks in machine to complex deep learning-based applications such as microstructural image analysis, image blurring, 3D characterization, structural zone diagram analysis, and dimensionality reduction.

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.




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