Machine Learning-Based Modelling in Atomic Layer Deposition Processes [Paperback]

This book describes the application of machine learning modelling approaches in atomic layer deposition and presents detailed information on modelling, optimization, and prediction of the behaviour and characteristics of ALD for improved process quality control.

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.

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 itsl–